1
|
Thote V, Dinesh S, Sharma S. Prediction of deleterious non-synonymous SNPs of human MDC1 gene: an in silico approach. Syst Biol Reprod Med 2024; 70:101-112. [PMID: 38630598 DOI: 10.1080/19396368.2024.2325699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 02/24/2024] [Indexed: 04/19/2024]
Abstract
MDC1 (Mediator of DNA damage Checkpoint protein 1) functions to facilitate the localization of numerous DNA damage response (DDR) components to DNA double-strand break sites. MDC1 is an integral component in preserving genomic stability and appropriate DDR regulation. There haven't been systematic investigations of MDC1 mutations that induce cancer and genomic instability. Variations in nsSNPs have the potential to modify the protein chemistry and their function. Describing functional SNPs in disease-associated genes presents a significant conundrum for investigators, it is possible to assess potential functional SNPs before conducting larger population examinations. Multiple sequences and structure-based bioinformatics strategies were implemented in the current in-silico investigation to discern potential nsSNPs of the MDC1 genes. The nsSNPs were identified with SIFT, SNAP2, Align GVGD, PolyPhen-2, and PANTHER, and their stability was determined with MUpro. The conservation, solvent accessibility, and structural effects of the mutations were identified with ConSurf, NetSurfP-2.0, and SAAFEC-SEQ respectively. Cancer-related analysis of the nsSNPs was conducted using cBioPortal and TCGA web servers. The present study appraised five nsSNPs (P1426T, P69S, P194R, P203L, and H131Y) as probably mutilating due to their existence in highly conserved regions and propensity to deplete protein stability. The nsSNPs P194R, P203L, and H131Y were concluded as deleterious and possibly damaging from the 5 prediction tools. The functional nsSNP P194R mutation is associated with skin cutaneous melanoma while no significant records were found for other nsSNPs. The present study concludes that the highly deleterious P194R mutations can potentially induce genomic instability and contribute to various cancers' pathogenesis. Developing drugs targeting these mutations can undoubtedly be advantageous in large population-based studies, particularly in the development of precision medicine.
Collapse
Affiliation(s)
| | - Susha Dinesh
- Department of Bioinformatics, BioNome, Bengaluru, India
| | - Sameer Sharma
- Department of Bioinformatics, BioNome, Bengaluru, India
| |
Collapse
|
2
|
Raas MWD, Dutheil JY. The rate of adaptive molecular evolution in wild and domesticated Saccharomyces cerevisiae populations. Mol Ecol 2024; 33:e16980. [PMID: 37157166 DOI: 10.1111/mec.16980] [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: 12/16/2022] [Revised: 04/22/2023] [Accepted: 04/26/2023] [Indexed: 05/10/2023]
Abstract
Through its fermentative capacities, Saccharomyces cerevisiae was central in the development of civilisation during the Neolithic period, and the yeast remains of importance in industry and biotechnology, giving rise to bona fide domesticated populations. Here, we conduct a population genomic study of domesticated and wild populations of S. cerevisiae. Using coalescent analyses, we report that the effective population size of yeast populations decreased since the divergence with S. paradoxus. We fitted models of distributions of fitness effects to infer the rate of adaptive (ω a ) and non-adaptive (ω na ) non-synonymous substitutions in protein-coding genes. We report an overall limited contribution of positive selection to S. cerevisiae protein evolution, albeit with higher rates of adaptive evolution in wild compared to domesticated populations. Our analyses revealed the signature of background selection and possibly Hill-Robertson interference, as recombination was found to be negatively correlated withω na and positively correlated withω a . However, the effect of recombination onω a was found to be labile, as it is only apparent after removing the impact of codon usage bias on the synonymous site frequency spectrum and disappears if we control for the correlation withω na , suggesting that it could be an artefact of the decreasing population size. Furthermore, the rate of adaptive non-synonymous substitutions is significantly correlated with the residue solvent exposure, a relation that cannot be explained by the population's demography. Together, our results provide a detailed characterisation of adaptive mutations in protein-coding genes across S. cerevisiae populations.
Collapse
Affiliation(s)
- Maximilian W D Raas
- Research Group Molecular Systems Evolution, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Julien Y Dutheil
- Research Group Molecular Systems Evolution, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Unité Mixte de Recherche 5554 Institut des Sciences de l'Evolution, CNRS, IRD, EPHE, Université de Montpellier, Montpellier, France
| |
Collapse
|
3
|
Zhang Y, Wang Y, Dou H, Wang S, Qu D, Peng X, Zou N, Yang L. Caffeine improves mitochondrial dysfunction in the white matter of neonatal rats with hypoxia-ischemia through deacetylation: a proteomic analysis of lysine acetylation. Front Mol Neurosci 2024; 17:1394886. [PMID: 38745725 PMCID: PMC11091324 DOI: 10.3389/fnmol.2024.1394886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 04/10/2024] [Indexed: 05/16/2024] Open
Abstract
Aims White matter damage (WMD) is linked to both cerebral palsy and cognitive deficits in infants born prematurely. The focus of this study was to examine how caffeine influences the acetylation of proteins within the neonatal white matter and to evaluate its effectiveness in treating white matter damage caused by hypoxia-ischemia. Main methods We employed a method combining affinity enrichment with advanced liquid chromatography and mass spectrometry to profile acetylation in proteins from the white matter of neonatal rats grouped into control (Sham), hypoxic-ischemic (HI), and caffeine-treated (Caffeine) groups. Key findings Our findings included 1,999 sites of lysine acetylation across 1,123 proteins, with quantifiable changes noted in 1,342 sites within 689 proteins. Analysis of these patterns identified recurring sequences adjacent to the acetylation sites, notably YKacN, FkacN, and G * * * GkacS. Investigation into the biological roles of these proteins through Gene Ontology analysis indicated their involvement in a variety of cellular processes, predominantly within mitochondrial locations. Further analysis indicated that the acetylation of tau (Mapt), a protein associated with microtubules, was elevated in the HI condition; however, caffeine treatment appeared to mitigate this over-modification, thus potentially aiding in reducing oxidative stress, inflammation in the nervous system, and improving mitochondrial health. Caffeine inhibited acetylated Mapt through sirtuin 2 (SITR2), promoted Mapt nuclear translocation, and improved mitochondrial dysfunction, which was subsequently weakened by the SIRT2 inhibitor, AK-7. Significance Caffeine-induced changes in lysine acetylation may play a key role in improving mitochondrial dysfunction and inhibiting oxidative stress and neuroinflammation.
Collapse
Affiliation(s)
- Yajun Zhang
- Department of Anesthesiology, Dalian Women and Children's Medical Group, Dalian, Liaoning, China
| | - Yuqian Wang
- Department of Pediatrics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Haiping Dou
- Department of Pediatrics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Shanshan Wang
- Department of Pediatrics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Danyang Qu
- Department of Pediatrics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Xin Peng
- Department of Pediatrics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Ning Zou
- Department of Pediatrics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Liu Yang
- Department of Pediatrics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| |
Collapse
|
4
|
Han K, Liu X, Sun G, Wang Z, Shi C, Liu W, Huang M, Liu S, Guo Q. Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms. Biochim Biophys Acta Gen Subj 2024:130601. [PMID: 38522679 DOI: 10.1016/j.bbagen.2024.130601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/17/2024] [Accepted: 03/15/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND Aberrant protein localization is a prominent feature in many human diseases and can have detrimental effects on the function of specific tissues and organs. High-throughput technologies, which continue to advance with iterations of automated equipment and the development of bioinformatics, enable the acquisition of large-scale data that are more pattern-rich, allowing for the use of a wider range of methods to extract useful patterns and knowledge from them. METHODS The proposed sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) model, designed to proficiently extract meaningful features from a vast repository of single-channel grayscale protein images for the purposes of protein localization analysis and clustering. Sc2promap incorporates a prediction head component enriched with supplementary protein annotations, along with the integration of a spatial-channel attention mechanism within the encoder to enables the generation of high-resolution protein localization maps that encapsulate the fundamental characteristics of cells, including elemental cellular localizations such as nuclear and non-nuclear domains. RESULTS Qualitative and quantitative comparisons were conducted across internal and external clustering evaluation metrics, as well as various facets of the clustering results. The study also explored different components of the model. The research outcomes conclusively indicate that, in comparison to previous methods, Sc2promap exhibits superior performance. CONCLUSIONS The amalgamation of the attention mechanism and prediction head components has led the model to excel in protein localization clustering and analysis tasks. GENERAL SIGNIFICANCE The model effectively enhances the capability to extract features and knowledge from protein fluorescence images.
Collapse
Affiliation(s)
- Kaitai Han
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Xi Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Guocheng Sun
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zijun Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Chaojing Shi
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Wu Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Mengyuan Huang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Shitou Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
| |
Collapse
|
5
|
Jadhav S, Vyavahare AJ, Sharma M. Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction. J Biomol Struct Dyn 2024:1-16. [PMID: 38444340 DOI: 10.1080/07391102.2023.2294386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 12/04/2023] [Indexed: 03/07/2024]
Abstract
Protein structure prediction (PSP) is a key concern in computational biology, which is considered a challenging task that is vital to determine the structure and the protein function since each protein possesses a definite shape, whereas the protein secondary structure prediction (PSSP) is the foundation for three-dimensional PSP. An Advanced hybrid ensemble deep predictor is utilized for predicting the structure of a protein using Long-Short Term Memory (LSTM), in which the performance of the predictor is improved for obtaining the features through the Salp-J Colony Optimization, which is developed by integrating the features of three optimizations the exploration behavior of Ulmaris, the immune system of virus colony and the teamwork of salp for solution update that helps to predict the accurate protein structure. The proposed method achieved the value of 99.1% accuracy, 99.5% sensitivity, 98.85% specificity, and 0.9% error at the 80% of training percentage 90 using CullPDB. Similarly, in Protein Net, the attained value of accuracy is 97.27%, sensitivity is 98.13%, specificity is 97%, and error is 2.7% concerning training percentage 90%.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Swati Jadhav
- Electronics and Telecommunication Department, D. Y. Patil College of Engineering, Akurdi, Pune, Maharashtra, India
| | - Arati J Vyavahare
- Electronics and Telecommunication Department, PES's Modern College of Engineering, Pune, Maharashtra, India
| | - Manish Sharma
- Electronics and Telecommunication Department, D. Y. Patil College of Engineering, Akurdi, Pune, Maharashtra, India
| |
Collapse
|
6
|
Saichuer P, Khrisanapant P, Senapin S, Rattanarojpong T, Somsoros W, Khunrae P, Sangsuriya P. Evaluate the potential use of TonB-dependent receptor protein as a subunit vaccine against Aeromonas veronii infection in Nile tilapia (Oreochromis niloticus). Protein Expr Purif 2024; 215:106412. [PMID: 38104792 DOI: 10.1016/j.pep.2023.106412] [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: 08/30/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
Aeromonas veronii is an emerging bacterial pathogen that causes serious systemic infections in cultured Nile tilapia (Oreochromis niloticus), leading to massive deaths. Therefore, there is an urgent need to identify effective vaccine candidates to control the spread of this emerging disease. TonB-dependent receptor (Tdr) of A. veronii, which plays a role in the virulence factor of the organism, could be useful in terms of protective antigens for vaccine development. This study aims to evaluate the potential use of Tdr protein as a novel subunit vaccine against A. veronii infection in Nile tilapia. The Tdr gene from A. veronii was cloned into the pET28b expression vector, and the recombinant protein was subsequently produced in Escherichia coli strain BL21 (DE3). Tdr was expressed as an insoluble protein and purified by affinity chromatography. Antigenicity test indicated that this protein was recognized by serum from A. veronii infected fish. When Nile tilapia were immunized with the Tdr protein, specific antibody levels increased significantly (p-value <0.05) at 7 days post-immunization (dpi), and peaked at 21 dpi compared to antibody levels at 0 dpi. Furthermore, bacterial agglutination activity was observed in the fish serum immunized with the Tdr protein, indicating that specific antibodies in the serum can detect Tdr on the bacterial cell surface. These results suggest that Tdr protein has potential as a vaccine candidate. However, challenging tests with A.veronii in Nile tilapia needs to be investigated to thoroughly evaluate its protective efficacy for future applications.
Collapse
Affiliation(s)
- Pornpavee Saichuer
- Department of Microbiology, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand
| | - Prit Khrisanapant
- Department of Microbiology, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand
| | - Saengchan Senapin
- Fish Health Platform, Center of Excellence for Shrimp Molecular Biology and Biotechnology (Centex Shrimp), Faculty of Science, Mahidol University, Bangkok, 10400, Thailand; National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathumthani, 12120, Thailand
| | - Triwit Rattanarojpong
- Department of Microbiology, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand
| | - Wasusit Somsoros
- Department of Microbiology, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand
| | - Pongsak Khunrae
- Department of Microbiology, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.
| | - Pakkakul Sangsuriya
- Aquatic Molecular Genetics and Biotechnology Research Team, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathumthani, 12120, Thailand.
| |
Collapse
|
7
|
Ektefaie Y, Shen A, Bykova D, Marin M, Zitnik M, Farhat M. Evaluating generalizability of artificial intelligence models for molecular datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581982. [PMID: 38464295 PMCID: PMC10925170 DOI: 10.1101/2024.02.25.581982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, i.e., similarity between train and test splits. We introduce Spectra, a spectral framework for comprehensive model evaluation. For a given model and input data, Spectra plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. We apply Spectra to 18 sequencing datasets with associated phenotypes ranging from antibiotic resistance in tuberculosis to protein-ligand binding to evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models, and convolutional neural networks. We show that SB and MB splits provide an incomplete assessment of model generalizability. With Spectra, we find as cross-split overlap decreases, deep learning models consistently exhibit a reduction in performance in a task- and model-dependent manner. Although no model consistently achieved the highest performance across all tasks, we show that deep learning models can generalize to previously unseen sequences on specific tasks. Spectra paves the way toward a better understanding of how foundation models generalize in biology.
Collapse
Affiliation(s)
- Yasha Ektefaie
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew Shen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
| | - Daria Bykova
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Maximillian Marin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
| | - Maha Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
8
|
Kulshreshtha A, Bhatnagar S. Structural effect of the H992D/H418D mutation of angiotensin-converting enzyme in the Indian population: implications for health and disease. J Biomol Struct Dyn 2024:1-18. [PMID: 38411559 DOI: 10.1080/07391102.2024.2321246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024]
Abstract
The Non synonymous SNPs (nsSNPs) of the renin-angiotensin-system (RAS) pathway, unique to the Indian population were investigated in view of its importance as an endocrine system. nsSNPs of the RAS pathway genes were mined from the IndiGenome database. Damaging nsSNPs were predicted using SIFT, PredictSNP, SNP and GO, Snap2 and Protein Variation Effect Analyzer. Loss of function was predicted based on protein stability change using I mutant, PremPS and CONSURF. The structural impact of the nsSNPs was predicted using HOPE and Missense3d followed by modeling, refinement, and energy minimization. Molecular Dynamics studies were carried out using Gromacsv2021.1. 23 Indian nsSNPs of the RAS pathway genes were selected for structural analysis and 8 were predicted to be damaging. Further sequence analysis showed that HEMGH zinc binding motif changes to HEMGD in somatic ACE-C domain (sACE-C) H992D and Testis ACE (tACE) H418D resulted in loss of zinc coordination, which is essential for enzymatic activity in this metalloprotease. There was a loss of internal interactions around the zinc coordination residues in the protein structural network. This was also confirmed by Principal Component Analysis, Free Energy Landscape and residue contact maps. Both mutations lead to broadening of the AngI binding cavity. The H992D mutation in sACE-C is likely to be favorable for cardiovascular health, but may lead to renal abnormalities with secondary impact on the heart. H418D in tACE is potentially associated with male infertility.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Akanksha Kulshreshtha
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, India
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, India
| |
Collapse
|
9
|
Bläsius K, Ludwig L, Knapp S, Flaßhove C, Sonnabend F, Keller D, Tacken N, Gao X, Kahveci-Türköz S, Grannemann C, Babendreyer A, Adrain C, Huth S, Baron JM, Ludwig A, Düsterhöft S. Pathological mutations reveal the key role of the cytosolic iRhom2 N-terminus for phosphorylation-independent 14-3-3 interaction and ADAM17 binding, stability, and activity. Cell Mol Life Sci 2024; 81:102. [PMID: 38409522 PMCID: PMC10896983 DOI: 10.1007/s00018-024-05132-3] [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: 11/29/2023] [Accepted: 01/15/2024] [Indexed: 02/28/2024]
Abstract
The protease ADAM17 plays an important role in inflammation and cancer and is regulated by iRhom2. Mutations in the cytosolic N-terminus of human iRhom2 cause tylosis with oesophageal cancer (TOC). In mice, partial deletion of the N-terminus results in a curly hair phenotype (cub). These pathological consequences are consistent with our findings that iRhom2 is highly expressed in keratinocytes and in oesophageal cancer. Cub and TOC are associated with hyperactivation of ADAM17-dependent EGFR signalling. However, the underlying molecular mechanisms are not understood. We have identified a non-canonical, phosphorylation-independent 14-3-3 interaction site that encompasses all known TOC mutations. Disruption of this site dysregulates ADAM17 activity. The larger cub deletion also includes the TOC site and thus also dysregulated ADAM17 activity. The cub deletion, but not the TOC mutation, also causes severe reductions in stimulated shedding, binding, and stability of ADAM17, demonstrating the presence of additional regulatory sites in the N-terminus of iRhom2. Overall, this study contrasts the TOC and cub mutations, illustrates their different molecular consequences, and reveals important key functions of the iRhom2 N-terminus in regulating ADAM17.
Collapse
Affiliation(s)
- Katharina Bläsius
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Lena Ludwig
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Sarah Knapp
- Institute of Biochemistry and Molecular Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Charlotte Flaßhove
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Friederike Sonnabend
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Diandra Keller
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Nikola Tacken
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Xintong Gao
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Selcan Kahveci-Türköz
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Caroline Grannemann
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Aaron Babendreyer
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Colin Adrain
- Patrick G Johnston Centre for Cancer Research, Queen's University, Belfast, Northern Ireland
| | - Sebastian Huth
- Department of Dermatology and Allergology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Jens Malte Baron
- Department of Dermatology and Allergology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Andreas Ludwig
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany
| | - Stefan Düsterhöft
- Institute of Molecular Pharmacology, Medical Faculty, RWTH Aachen University, Wendlingweg 2, 52074, Aachen, Germany.
| |
Collapse
|
10
|
Zhong G, Deng L. ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies. J Chem Inf Model 2024; 64:1092-1104. [PMID: 38277774 DOI: 10.1021/acs.jcim.3c01860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
Novel therapeutic alternatives for cancer treatment are increasingly attracting global research attention. Although chemotherapy remains a primary clinical solution, it often results in significant side effects for patients. In recent years, anticancer peptides (ACPs) have emerged as promising candidates for highly specific anticancer drugs, and a number of computational approaches have been developed to identify ACPs. However, existing methods do not recognize specific types of anticancer function. In this article, we propose ACPScanner, an integrated approach to predict ACPs and non-ACPs at first and then predict several specific activity types for potential ACPs. We incorporate sequential, physicochemical properties, secondary structural information, and deep representation learning embeddings which are generated from artificial intelligence methods to build feature space. Customized deep learning and statistical learning methods are combined to form an integral architecture for the comprehensive two-level prediction task. To the best of our knowledge, ACPScanner is the first approach for specific ACP activity prediction. The comparative evaluation illustrates that ACPScanner achieves competitive prediction performance in both prediction phases in independent testings. We establish a web server at http://acpscanner.denglab.org to provide convenient usage of ACPScanner and make the predictive framework, source code, and data sets publicly available.
Collapse
Affiliation(s)
- Guolun Zhong
- School of Computer Science and Engineering, Central South University, Changsha 410000, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410000, China
| |
Collapse
|
11
|
Høie MH, Gade FS, Johansen J, Würtzen C, Winther O, Nielsen M, Marcatili P. DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations. Front Immunol 2024; 15:1322712. [PMID: 38390326 PMCID: PMC10882062 DOI: 10.3389/fimmu.2024.1322712] [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: 10/16/2023] [Accepted: 01/08/2024] [Indexed: 02/24/2024] Open
Abstract
Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.
Collapse
Affiliation(s)
- Magnus Haraldson Høie
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Frederik Steensgaard Gade
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Julie Maria Johansen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Charlotte Würtzen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Ole Winther
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
- Center for Genomic Medicine, Rigshospitalet (Copenhagen University Hospital), Copenhagen, Denmark
- Department of Biology, Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Paolo Marcatili
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| |
Collapse
|
12
|
Pandey P, Alexov E. Most Monogenic Disorders Are Caused by Mutations Altering Protein Folding Free Energy. Int J Mol Sci 2024; 25:1963. [PMID: 38396641 PMCID: PMC10888012 DOI: 10.3390/ijms25041963] [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: 12/29/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
Revealing the molecular effect that pathogenic missense mutations have on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast targeted drug development at a low cost. Here, we report an updated database of monogenic disorders (MOGEDO), which includes 768 proteins and the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG, but do not make protein over-stable, since over-stable protein may be dysfunctional. We demonstrate that, by using ΔΔG and predicted solvent exposure of the mutation site, one can develop a predictive method that distinguishes pathogenic from benign mutations with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic-hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp, and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.
Collapse
Affiliation(s)
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA;
| |
Collapse
|
13
|
Shoushtari M, Rismani E, Salehi-Vaziri M, Azadmanesh K. Structure-based evaluation of the envelope domain III-nonstructural protein 1 (EDIII-NS1) fusion as a dengue virus vaccine candidate. J Biomol Struct Dyn 2024:1-19. [PMID: 38319049 DOI: 10.1080/07391102.2024.2311350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024]
Abstract
The lack of effective medicines or vaccines, combined with climate change and other environmental factors, annually subjects a significant proportion of the world's inhabitants to the risk of dengue virus (DENV) infection. These conditions increase the likelihood of exposure to mosquito-borne diseases such as dengue fever. Hence, many research approaches tend to develop efficient vaccine candidates against the dengue virus. Therefore, we used immunoinformatics and bioinformatics to design a construction for developing a candidate vaccine against dengue virus serotypes. In this study, the in silico structure, containing the non-structural protein 1 region (NS1) (consensus and epitope), the envelope domain III protein (EDIII) as the structural part of the virus construction, and the bc-loop of envelope domain II (EDII) as the neutralizing and protected epitope, were employed. We utilized in silico tools to enhance the immunogenicity and effectiveness of dengue virus vaccine candidates. Evaluations included refining and validating physicochemical characteristics, B and T-cell epitopes, homology modeling, and the three-dimensional structure to assess the designed vaccine's quality. In silico results for tertiary structure prediction and validation revealed high-quality modeling for all vaccine constructs. Additionally, the instructed model demonstrated stability throughout molecular dynamics simulation. The results of the immune simulation suggested that the titers of IgG and IgM could be raised to desirable values following injection into in vivo models. It can be concluded that the designed construct effectively induce humoral and cellular immunity and can be proposed as effective vaccine candidate against four dengue serotypes.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
| | - Elham Rismani
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Mostafa Salehi-Vaziri
- Department of Arboviruses and Viral Hemorrhagic Fevers (National Reference Laboratory), Pasteur Institute of Iran, Tehran, Iran
| | | |
Collapse
|
14
|
Danoff JS, Carter CS, Gordevičius J, Milčiūtė M, Brooke RT, Connelly JJ, Perkeybile AM. Maternal oxytocin treatment at birth increases epigenetic age in male offspring. Dev Psychobiol 2024; 66:e22452. [PMID: 38533486 PMCID: PMC10963051 DOI: 10.1002/dev.22452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/26/2023] [Indexed: 03/28/2024]
Abstract
Exogenous oxytocin (OT) is widely used to induce or augment labor with little understanding of the impact on offspring development. In rodent models, including the prairie vole (Microtus ochrogaster), it has been shown that oxytocin administered to mothers can affect the nervous system of the offspring with long lasting behavioral effects especially on sociality. Here, we examined the hypothesis that perinatal oxytocin exposure could have epigenetic and transcriptomic consequences. Prairie voles were exposed to exogenous oxytocin, through injections given to the mother just prior to birth, and were studied at the time of weaning. The outcome of this study revealed increased epigenetic age in oxytocin-exposed animals compared to the saline-exposed group. Oxytocin exposure led to 900 differentially methylated CpG sites (annotated to 589 genes), and 2 CpG sites (2 genes) remained significantly different after correction for multiple comparisons. Differentially methylated CpG sites were enriched in genes known to be involved in regulation of gene expression and neurodevelopment. Using RNA-sequencing we also found 217 nominally differentially expressed genes (p<0.05) in nucleus accumbens, a brain region involved in reward circuitry and social behavior; after corrections for multiple comparisons 6 genes remained significantly differentially expressed. Finally, we found that maternal oxytocin administration led to widespread alternative splicing in the nucleus accumbens. These results indicate that oxytocin exposure during birth may have long lasting epigenetic consequences. A need for further investigation of how oxytocin administration impacts development and behavior throughout the lifespan is supported by these outcomes.
Collapse
Affiliation(s)
- Joshua S Danoff
- Department of Psychology, University of Virginia, Charlottesville, VA
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ
| | - C Sue Carter
- Department of Psychology, University of Virginia, Charlottesville, VA
- Kinsey Institute, Indiana University, Bloomington IN
| | | | | | | | | | - Allison M Perkeybile
- Department of Psychology, University of Virginia, Charlottesville, VA
- Kinsey Institute, Indiana University, Bloomington IN
| |
Collapse
|
15
|
Peracha O. PS4: a next-generation dataset for protein single-sequence secondary structure prediction. Biotechniques 2024; 76:63-70. [PMID: 37997848 DOI: 10.2144/btn-2023-0024] [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] [Indexed: 11/25/2023] Open
Abstract
Protein secondary structure prediction is a subproblem of protein folding. A light-weight algorithm capable of accurately predicting secondary structure from only the protein residue sequence could provide useful input for tertiary structure prediction, alleviating the reliance on multiple sequence alignments typically seen in today's best-performing models. Unfortunately, existing datasets for secondary structure prediction are small, creating a bottleneck. We present PS4, a dataset of 18,731 nonredundant protein chains and their respective secondary structure labels. Each chain is identified, and the dataset is nonredundant against other secondary structure datasets commonly seen in the literature. We perform ablation studies by training secondary structure prediction algorithms on the PS4 training set and obtains state-of-the-art accuracy on the CB513 test set in zero shots.
Collapse
Affiliation(s)
- Omar Peracha
- Department for Continuing Education, University of Oxford, Rewley House, 1 Wellington Square, Oxford, OX1 2JA, United Kingdom
| |
Collapse
|
16
|
Ding T, Yang YH, Wang QC, Wu Y, Han R, Zhang XT, Kong J, Yang JT, Liu JF. Global profiling of protein lactylation in Caenorhabditis elegans. Proteomics 2024; 24:e2300185. [PMID: 37847886 DOI: 10.1002/pmic.202300185] [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: 04/12/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 10/19/2023]
Abstract
Lactylation, as a novel posttranslational modification, is essential for studying the functions and regulation of proteins in physiological and pathological processes, as well as for gaining in-depth knowledge on the occurrence and development of many diseases, including tumors. However, few studies have examined the protein lactylation of one whole organism. Thus, we studied the lactylation of global proteins in Caenorhabditis elegans to obtain an in vivo lactylome. Using an MS-based platform, we identified 1836 Class I (localization probabilities > 0.75) lactylated sites in 487 proteins. Bioinformatics analysis showed that lactylated proteins were mainly located in the cytoplasm and involved in the tricarboxylic acid cycle (TCA cycle) and other metabolic pathways. Then, we evaluated the conservation of lactylation in different organisms. In total, 41 C. elegans proteins were lactylated and homologous to lactylated proteins in humans and rats. Moreover, lactylation on H4K80 was conserved in three species. An additional 238 lactylated proteins were identified in C. elegans for the first time. This study establishes the first lactylome database in C. elegans and provides a basis for studying the role of lactylation.
Collapse
Affiliation(s)
- Tao Ding
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
- School of Basic Medical Science, Guizhou Medical University, Guiyang, China
| | - Ye-Hong Yang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Qiao-Chu Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Yue Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Rong Han
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Xu-Tong Zhang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jie Kong
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jun-Tao Yang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
- School of Basic Medical Science, Guizhou Medical University, Guiyang, China
- Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang-Feng Liu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences Chinese Academy ofMedical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
- Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
17
|
Aguech A, Sfaihi L, Alila-Fersi O, Kolsi R, Tlili A, Kammoun T, Fendri A, Fakhfakh F. A novel homozygous PIGO mutation associated with severe infantile epileptic encephalopathy, profound developmental delay and psychomotor retardation: structural and 3D modelling investigations and genotype-phenotype correlation. Metab Brain Dis 2023; 38:2665-2678. [PMID: 37656370 DOI: 10.1007/s11011-023-01276-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023]
Abstract
The PIGO gene encodes the GPI-ethanolamine phosphate transferase 3, which is crucial for the final synthetic step of the glycosylphosphatidylinositol-anchor serving to attach various proteins to their cell surface. These proteins are intrinsic for normal neuronal and embryonic development. In the current research work, a clinical investigation was conducted on a patient from a consanguineous family suffering from epileptic encephalopathy, characterized by severe seizures, developmental delay, hypotonia, ataxia and hyperphosphatasia. Molecular analysis was performed using Whole Exome Sequencing (WES). The molecular investigation revealed a novel homozygous variant c.1132C > T in the PIGO gene, in which a highly conserved Leucine was changed to a Phenylalanine (p.L378F). To investigate the impact of the non-synonymous mutation, a 3D structural model of the PIGO protein was generated using the AlphaFold protein structure database as a resource for template-based tertiary structure modeling. A structural analysis by applying some bioinformatic tools on both variants 378L and 378F models predicted the pathogenicity of the non-synonymous mutation and its potential functional and structural effects on PIGO protein. We also discussed the phenotypic and genotypic variability associated with the PIGO deficiency. To our best knowledge, this is the first report of a patient diagnosed with infantile epileptic encephalopathy showing a high elevation of serum alkaline phosphatase level. Our findings, therefore, widen the genotype and phenotype spectrum of GPI-anchor deficiencies and broaden the cohort of patients with PIGO associated epileptic encephalopathy with an elevated serum alkaline phosphatase level.
Collapse
Affiliation(s)
- Ameni Aguech
- Molecular Genetics and Functional Laboratory, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia.
| | - Lamia Sfaihi
- Pediatrics Department, Hedi Chaker University Hospital, 3029, Sfax, Tunisia
- Faculty of Medecine of Sfax, University of Sfax, Avenue Magida Boulila, 3029, Sfax, Tunisia
| | - Olfa Alila-Fersi
- Molecular Genetics and Functional Laboratory, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Roeya Kolsi
- Pediatrics Department, Hedi Chaker University Hospital, 3029, Sfax, Tunisia
- Faculty of Medecine of Sfax, University of Sfax, Avenue Magida Boulila, 3029, Sfax, Tunisia
| | - Abdelaziz Tlili
- Department of Applied Biology, College of Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Thouraya Kammoun
- Pediatrics Department, Hedi Chaker University Hospital, 3029, Sfax, Tunisia
- Faculty of Medecine of Sfax, University of Sfax, Avenue Magida Boulila, 3029, Sfax, Tunisia
| | - Ahmed Fendri
- Laboratory of Biochemistry and Enzymatic Engineering of Lipases, Engineering National School of Sfax (ENIS), University of Sfax, Sfax, Tunisia
| | - Faiza Fakhfakh
- Molecular Genetics and Functional Laboratory, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia.
| |
Collapse
|
18
|
Murthy TPK, Shukla R, Durga Prasad N, Swetha P, Shreyas S, Singh TR, Pattabiraman R, Nair SS, Mathew BB, Kumar KM. Comprehensive analysis of non-synonymous missense SNPs of human galactose mutarotase (GALM) gene: an integrated computational approach. J Biomol Struct Dyn 2023; 41:11178-11192. [PMID: 36591702 DOI: 10.1080/07391102.2022.2160813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 12/15/2022] [Indexed: 01/03/2023]
Abstract
Missense Non-synonymous single nucleotide polymorphisms (nsSNPs) of Galactose Mutarotase (GALM) are associated with the Novel type of Galactosemia (Galactosemia type 4) together with symptoms such as high blood galactose levels and eye cataracts. The objective of the present study was to identify deleterious nsSNPs of GALM recorded on the dbSNP database through comprehensive insilico analysis. Among the 319 missense nsSNPs reported, various insilco tools predicted R78S, R82G, A163E, P210S, Y281C, E307G and F339C as the most deleterious mutations. Structural analysis, PTM analysis and molecular dynamics simulations (MDS) were carried out to understand the effect of these mutations on the structural and physicochemical properties of the GALM protein. The residues R82G and E307G were found to be part of the binding site that resulted in decreased surface accessibility. Replacing the charged wild-type residue with a neutral mutant type affected its substrate binding. All 7 mutations were found to increase the rigidity of the protein structure, which is unfavorable during ligand binding. The mutation F339E made the protein structure more rigid than all the other mutations. Y281 is a phosphorylated site, and therefore, less significant structural changes were observed when compared to other mutations; however, it may have significant differences in the usual functioning of the protein. In summary, the structural and functional analysis of missense SNPs of GALM is important to reduce the number of potential mutations to be evaluated in vitro to understand the association with some genetic diseases.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- T P Krishna Murthy
- Department of Biotechnology, Ramaiah Institute of Technology, Bengaluru, Karnataka, India
| | - Rohit Shukla
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan, Himachal Pradesh, India
| | - N Durga Prasad
- Department of Biotechnology, Ramaiah Institute of Technology, Bengaluru, Karnataka, India
| | - Praveen Swetha
- Department of Biotechnology, Ramaiah Institute of Technology, Bengaluru, Karnataka, India
| | - S Shreyas
- Department of Biotechnology, Ramaiah Institute of Technology, Bengaluru, Karnataka, India
| | - Tiratha Raj Singh
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan, Himachal Pradesh, India
| | - Ramya Pattabiraman
- Department of Biotechnology, Ramaiah Institute of Technology, Bengaluru, Karnataka, India
| | - Shishira S Nair
- Department of Biotechnology, Ramaiah Institute of Technology, Bengaluru, Karnataka, India
| | - Blessy B Mathew
- Department of Biotechnology, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, Inida
| | - K M Kumar
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, India
| |
Collapse
|
19
|
Teufel F, Gíslason MH, Almagro Armenteros JJ, Johansen A, Winther O, Nielsen H. GraphPart: homology partitioning for biological sequence analysis. NAR Genom Bioinform 2023; 5:lqad088. [PMID: 37850036 PMCID: PMC10578201 DOI: 10.1093/nargab/lqad088] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/25/2023] [Accepted: 09/19/2023] [Indexed: 10/19/2023] Open
Abstract
When splitting biological sequence data for the development and testing of predictive models, it is necessary to avoid too-closely related pairs of sequences ending up in different partitions. If this is ignored, performance of prediction methods will tend to be overestimated. Several algorithms have been proposed for homology reduction, where sequences are removed until no too-closely related pairs remain. We present GraphPart, an algorithm for homology partitioning that divides the data such that closely related sequences always end up in the same partition, while keeping as many sequences as possible in the dataset. Evaluation of GraphPart on Protein, DNA and RNA datasets shows that it is capable of retaining a larger number of sequences per dataset, while providing homology separation on a par with reduction approaches.
Collapse
Affiliation(s)
- Felix Teufel
- Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark
- Digital Science & Innovation, Novo Nordisk A/S, 2760 Måløv, Denmark
| | - Magnús Halldór Gíslason
- Department of Genomic Medicine, Copenhagen University Hospital/Rigshospitalet, 2100 Copenhagen, Denmark
| | - José Juan Almagro Armenteros
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | | | - Ole Winther
- Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark
- Department of Genomic Medicine, Copenhagen University Hospital/Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Henrik Nielsen
- Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| |
Collapse
|
20
|
Grima N, Liu S, Southwood D, Henden L, Smith A, Lee A, Rowe DB, D'Silva S, Blair IP, Williams KL. RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery. Neuropathol Appl Neurobiol 2023; 49:e12943. [PMID: 37818590 PMCID: PMC10946588 DOI: 10.1111/nan.12943] [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: 06/08/2023] [Revised: 10/02/2023] [Accepted: 10/04/2023] [Indexed: 10/12/2023]
Abstract
AIM Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease with limited therapeutic options. A key factor limiting the development of effective therapeutics is the lack of disease biomarkers. We sought to assess whether biomarkers for diagnosis, prognosis or cohort stratification could be identified by RNA sequencing (RNA-seq) of ALS patient peripheral blood. METHODS Whole blood RNA-seq data were generated for 96 Australian sporadic ALS (sALS) cases and 48 healthy controls (NCBI GEO accession GSE234297). Differences in sALS-control gene expression, transcript usage and predicted leukocyte proportions were assessed, with pathway analysis used to predict the activity state of biological processes. Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms were applied to search for diagnostic and prognostic gene expression patterns. Unsupervised clustering analysis was employed to determine whether sALS patient subgroups could be detected. RESULTS Two hundred and forty-five differentially expressed genes were identified in sALS patients relative to controls, with enrichment of immune, metabolic and stress-related pathways. sALS patients also demonstrated switches in transcript usage across a small set of genes. We established a classification model that distinguished sALS patients from controls with an accuracy of 78% (sensitivity: 79%, specificity: 75%) using the expression of 20 genes. Clustering analysis identified four patient subgroups with gene expression signatures and immune cell proportions reflective of distinct peripheral effects. CONCLUSIONS Our findings suggest that peripheral blood RNA-seq can identify diagnostic biomarkers and distinguish molecular subtypes of sALS patients however, its prognostic value requires further investigation.
Collapse
Affiliation(s)
- Natalie Grima
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Sidong Liu
- Centre for Health InformaticsFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Dean Southwood
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Lyndal Henden
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Andrew Smith
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Albert Lee
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Dominic B. Rowe
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Susan D'Silva
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Ian P. Blair
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| | - Kelly L. Williams
- Motor Neuron Disease Research CentreMacquarie Medical SchoolFaculty of Medicine, Health and Human SciencesMacquarie UniversitySydneyNSWAustralia
| |
Collapse
|
21
|
Azmi MB, Jawed A, Ahmed SDH, Naeem U, Feroz N, Saleem A, Sardar K, Qureshi SA, Azim MK. Understanding the impact of structural modifications at the NNAT gene's post-translational acetylation site: in silico approach for predicting its drug-interaction role in anorexia nervosa. Eat Weight Disord 2023; 28:97. [PMID: 37987927 PMCID: PMC10663277 DOI: 10.1007/s40519-023-01618-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 10/18/2023] [Indexed: 11/22/2023] Open
Abstract
PURPOSE Anorexia nervosa (AN) is a neuropsychological public health concern with a socially disabling routine and affects a person's healthy relationship with food. The role of the NNAT (Neuronatin) gene in AN is well established. The impact of mutation at the protein's post-translational modification (PTM) site has been exclusively associated with the worsening of the protein's biochemical dynamics. METHODS To understand the relationship between genotype and phenotype, it is essential to investigate the appropriate molecular stability of protein required for proper biological functioning. In this regard, we investigated the PTM-acetylation site of the NNAT gene in terms of 19 other specific amino acid probabilities in place of wild type (WT) through various in silico algorithms. Based on the highest pathogenic impact computed through the consensus classifier tool, we generated 3 residue-specific (K59D, P, W) structurally modified 3D models of NNAT. These models were further tested through the AutoDock Vina tool to compute the molecular drug binding affinities and inhibition constant (Ki) of structural variants and WT 3D models. RESULTS With trained in silico machine learning algorithms and consensus classifier; the three structural modifications (K59D, P, W), which were also the most deleterious substitution at the acetylation site of the NNAT gene, showed the highest structural destabilization and decreased molecular flexibility. The validation and quality assessment of the 3D model of these structural modifications and WT were performed. They were further docked with drugs used to manage AN, it was found that the ΔGbind (kcal/mol) values and the inhibition constants (Ki) were relatively lower in structurally modified models as compared to WT. CONCLUSION We concluded that any future structural variation(s) at the PTM-acetylation site of the NNAT gene due to possible mutational consequences, will serve as a basis to explore its relationship with the propensity of developing AN. LEVEL OF EVIDENCE No level of evidence-open access bioinformatics research.
Collapse
Affiliation(s)
- Muhammad Bilal Azmi
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan.
| | - Areesha Jawed
- Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Syed Danish Haseen Ahmed
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Unaiza Naeem
- Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Nazia Feroz
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Arisha Saleem
- Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Kainat Sardar
- Department of Biochemistry, University of Karachi, Karachi, Pakistan
- Department of Chemistry, Bahria College NORE-1, Karachi, Pakistan
| | | | - M Kamran Azim
- Department of Biosciences, Mohammad Ali Jinnah University, Karachi, Pakistan
| |
Collapse
|
22
|
Azmi MB, Sehgal SA, Asif U, Musani S, Abedin MFE, Suri A, Ahmed SDH, Qureshi SA. Genetic insights into obesity: in silico identification of pathogenic SNPs in MBOAT4 gene and their structural molecular dynamics consequences. J Biomol Struct Dyn 2023:1-17. [PMID: 37921712 DOI: 10.1080/07391102.2023.2274970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/18/2023] [Indexed: 11/04/2023]
Abstract
Membrane Bound O-Acyltransferase Domain-Containing 4 (MBOAT4) protein catalyzes ghrelin acylation, leading to prominent ghrelin activity, hence characterizing its role as an anti-obesity target. We extracted 625 exonic SNPs from the ENSEMBL database and one phenotype-based missense mutation associated with obesity (A46T) from the HGMD (Human Gene Mutation Database). These were differentiated on deleterious missense SNPs of the MBOAT4 gene through MAF (minor allele frequency: <0.01) cut-off criteria in relation to some bioinformatics-based supervised machine learning tools. We found 8 rare-coding and harmful missense SNPs. The consensus classifier (PredictSNP) tool predicted that the SNP (G57S, C: rs561065025) was the most pathogenic. Several trained in silico algorithms have predicted decreased protein stability [ΔΔG (kcal/mol)] function in the presence of these rare-coding pathogenic mutations in the MBOAT4 gene. Then, a stereochemical quality check (i.e. validation and assessment) of the 3D model was performed, followed by a blind cavity docking approach, used to search for druggable cavities and molecular interactions with citrus flavonoids of the Rutaceae family, ranked with energetic estimations. Significant interactions with Phloretin 3',5'-Di-C-Glucoside were also observed at R304, W306, N307, A311, L314 and H338 with (iGEMDOCK: -95.82 kcal/mol and AutoDock: -7.80 kcal/mol). The RMSD values and other variables of MD simulation analyses on this protein further validated its significant interactions with the above flavonoids. The MBOAT4 gene and its molecular interactions could serve as an interventional future anti-obesity target. The current study's findings will benefit future prospects for large population-based studies and drug development, particularly for generating personalized medicine.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Muhammad Bilal Azmi
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Sheikh Arslan Sehgal
- Department of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Uzma Asif
- Department of Biochemistry, Medicine Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Sarah Musani
- Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | | | - Azeema Suri
- Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Syed Danish Haseen Ahmed
- Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | | |
Collapse
|
23
|
Azmi MB, Ahmed A, Ahmed TF, Imtiaz F, Asif U, Zaman U, Khan KA, Sherwani AK. Transcript-Level In Silico Analysis of Alzheimer's Disease-Related Gene Biomarkers and Their Evaluation with Bioactive Flavonoids to Explore Therapeutic Interactions. ACS OMEGA 2023; 8:40695-40712. [PMID: 37929088 PMCID: PMC10621018 DOI: 10.1021/acsomega.3c05769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 09/25/2023] [Indexed: 11/07/2023]
Abstract
Alzheimer's disease (AD) is a progressive brain disorder that can significantly affect the quality of life. We used a variety of in silico tools to investigate the transcript-level mutational impact of exonic missense rare variations (single nucleotide polymorphisms, SNPs) on protein function and to identify potential druggable protein cavities that correspond to potential therapeutic targets for the management of AD. According to the NIA-AA (National Institute on Aging-Alzheimer's Association) framework, we selected three AD biomarker genes (APP, NEFL, and MAPT). We systematically screened transcript-level exonic rare SNPs from these genes with a minor allele frequency of 1% in 1KGD (1000 Genomes Project Database) and gnomAD (Genome Aggregation Database). With downstream functional effect predictions, a single variation (rs182024939: K > N) of the MAPT gene with nine transcript SNPs was identified as the most pathogenic variation from the large dataset of mutations. The machine learning consensus classifier predictor categorized these transcript-level SNPs as the most deleterious variations, resulting in a large decrease in protein structural stability (ΔΔG kcal/mol). The bioactive flavonoid library was screened for drug-likeness and toxicity risk. Virtual screening of eligible flavonoids was performed using the MAPT protein. Identification of druggable protein-binding cavities showed VAL305, GLU655, and LYS657 as consensus-interacting residues present in the MAPT-docked top-ranked flavonoid compounds. The MM/PB(GB)SA analysis indicated hesperetin (-5.64 kcal/mol), eriodictyol (-5.63 kcal/mol), and sakuranetin (-5.60 kcal/mol) as the best docked flavonoids with the near-native binding pose. The findings of this study provide important insights into the potential of hesperetin as a promising flavonoid that can be utilized for further rational drug design and lead optimization to open new gateways in the field of AD therapeutics.
Collapse
Affiliation(s)
- Muhammad Bilal Azmi
- Department
of Biochemistry, Dow Medical College, Dow
University of Health Sciences, Karachi 74400, Pakistan
| | - Affan Ahmed
- Dow
Medical College, Dow University of Health
Sciences, Karachi 74400, Pakistan
| | - Tehniat Faraz Ahmed
- Department
of Biochemistry, Dow International Dental College, Dow University of Health Sciences, Karachi 75460, Pakistan
| | - Fauzia Imtiaz
- Department
of Biochemistry, Dow Medical College, Dow
University of Health Sciences, Karachi 74400, Pakistan
| | - Uzma Asif
- Department
of Biochemistry, Medicine Program, Batterjee
Medical College, Jeddah 21442, Saudi Arabia
| | - Uzma Zaman
- Department
of Biochemistry, Dow International Medical College, Dow University of Health Sciences, Karachi 74200, Pakistan
| | - Khalid Ali Khan
- Unit of Bee
Research and Honey Production, Research Center for Advanced Materials
Science (RCAMS), King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
- Applied
College, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
| | - Asif Khan Sherwani
- Research
and Development Unit, Jamjoom Pharmaceuticals
Co. Ltd, Jeddah 21442, Saudi Arabia
| |
Collapse
|
24
|
Pandey P, Alexov E. Most monogenic disorders are caused by mutations altering protein folding free energy. RESEARCH SQUARE 2023:rs.3.rs-3442589. [PMID: 37886551 PMCID: PMC10602188 DOI: 10.21203/rs.3.rs-3442589/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Revealing the molecular effect that pathogenic missense mutations cause on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast, targeted drug development at a low cost. Here, we report a database of monogenic disorders (MOGEDO), which includes 768 proteins, the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in-silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG but not to make protein over-stable since over-stable protein may be dysfunctional. We demonstrate that using ΔΔG and predicted solvent exposure of the mutation site; one can develop a predictive method that distinguishes pathogenic from benign mutation with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic-hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), and the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.
Collapse
|
25
|
Broz M, Jukič M, Bren U. Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning. Molecules 2023; 28:7046. [PMID: 37894526 PMCID: PMC10609058 DOI: 10.3390/molecules28207046] [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: 09/01/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone ϕ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the ϕ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies.
Collapse
Affiliation(s)
- Matic Broz
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
| | - Marko Jukič
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška ulica 8, SI-6000 Koper, Slovenia
- Institute of Environmental Protection and Sensors, Beloruska ulica 7, SI-2000 Maribor, Slovenia
| | - Urban Bren
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška ulica 8, SI-6000 Koper, Slovenia
- Institute of Environmental Protection and Sensors, Beloruska ulica 7, SI-2000 Maribor, Slovenia
| |
Collapse
|
26
|
Abdollahi S, Raoufi Z. A novel vaccine candidate against A. baumannii based on a new OmpW family protein (OmpW2); structural characterization, antigenicity and epitope investigation, and in-vivo analysis. Microb Pathog 2023; 183:106317. [PMID: 37611777 DOI: 10.1016/j.micpath.2023.106317] [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: 04/28/2023] [Revised: 06/06/2023] [Accepted: 08/20/2023] [Indexed: 08/25/2023]
Abstract
A. baumannii is an MDR pathogen whose SARS-CoV-2 has recently increased its mortality rate in hospitalized patients. So, the virulence factors investigation and design of a vaccine against this bacterium seem to be critical. In this regard, the OmpW2 protein was structurally characterized by this study, and its B-T cell epitopes were mapped by bioinformatic tools. In-vivo analyses were employed to verify the immunogenicity of this protein and its selected epitopes. The results indicated that OmpW2 is a conserved virulent antigen, not toxic for the host, and not similar to the human or mouse proteome. A putative interaction between OmpW2 and a Fe-S-cluster redox enzyme was detected. Based on the results, OmpW2 belongs to the OmpW superfamily and eight beta sheets have been predicted in its tight beta-barrel structure. Several exposed epitopes were detected in the OmpW2 sequence and structure, and a sub-unit potential vaccine was generated based on the epitopes. The ELISA results indicated that after the second booster vaccination of BALB/c mice with the whole OmpW2 protein or its sub-unit fragment, the IgG titer significantly raised (p < 0.05). The mortality rate and the bacterial burden in the lung, liver, kidney, and spleen in both passive and active immunized mice were significantly decreased (p ≤ 0.001). In-vivo experiments confirmed that the OmpW2 whole protein and its sub-unit fragment induce the host immune system and can be applied to design a commercial vaccine or diagnostic kit.
Collapse
Affiliation(s)
- Sajad Abdollahi
- Department of Biology, Faculty of Basic Science, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran.
| | - Zeinab Raoufi
- Department of Biology, Faculty of Basic Science, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran
| |
Collapse
|
27
|
Avsar O. Identification of the effects of pathogenic genetic variations of human CYP2C9 and CYP2D6: an in silico approach. NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS 2023; 43:356-376. [PMID: 37747773 DOI: 10.1080/15257770.2023.2262519] [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: 07/28/2023] [Accepted: 09/19/2023] [Indexed: 09/26/2023]
Abstract
Genetic variations in the human cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily D member 6 (CYP2D6) genes may affect drug metabolism and lead to alterations in phenotypes. Genetic variations are associated with toxicity, adverse drug reactions, inefficient treatment. Various in silico tools were combined to investigate the deleterious effects of missense non-synonymous single nucleotide polymorphisms (nsSNPs) of the human CYP2C9 and CYP2D6. The structural and functional effects of the high-risk non-synonymous SNPs in the human CYP2C9 and CYP2D6 were predicted by numerous computational mutation analysis methods. Out of 24 pathogenic missense SNPs in the CYP2C9, 22 nsSNPs had a decreasing effect on protein stability and 13 SNPs were showed to be located at conserved positions. Out of 27 high-risk deleterious non-synonymous SNPs in the human CYP2D6, 21 SNPs decreased protein stability and 16 nsSNPs were predicted to be positioned at conserved regions. Our present study suggests that the identified functional SNPs may affect drug metabolism associated with CYP2C9 and CYP2D6 enzymes.
Collapse
Affiliation(s)
- Orcun Avsar
- Department of Molecular Biology and Genetics, Faculty of Arts and Sciences, Hitit University, Corum, Türkiye
| |
Collapse
|
28
|
Waury K, de Wit R, Verberk IMW, Teunissen CE, Abeln S. Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery. J Proteome Res 2023; 22:3068-3080. [PMID: 37606934 PMCID: PMC10476268 DOI: 10.1021/acs.jproteome.3c00366] [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: 06/19/2023] [Indexed: 08/23/2023]
Abstract
Cerebrospinal fluid (CSF) is an essential matrix for the discovery of neurological disease biomarkers. However, the high dynamic range of protein concentrations in CSF hinders the detection of the least abundant protein biomarkers by untargeted mass spectrometry. It is thus beneficial to gain a deeper understanding of the secretion processes within the brain. Here, we aim to explore if and how the secretion of brain proteins to the CSF can be predicted. By combining a curated CSF proteome and the brain elevated proteome of the Human Protein Atlas, brain proteins were classified as CSF or non-CSF secreted. A machine learning model was trained on a range of sequence-based features to differentiate between CSF and non-CSF groups and effectively predict the brain origin of proteins. The classification model achieves an area under the curve of 0.89 if using high confidence CSF proteins. The most important prediction features include the subcellular localization, signal peptides, and transmembrane regions. The classifier generalized well to the larger brain detected proteome and is able to correctly predict novel CSF proteins identified by affinity proteomics. In addition to elucidating the underlying mechanisms of protein secretion, the trained classification model can support biomarker candidate selection.
Collapse
Affiliation(s)
- Katharina Waury
- Department
of Computer Science, Vrije Universiteit
Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Renske de Wit
- Department
of Computer Science, Vrije Universiteit
Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Inge M. W. Verberk
- Neurochemistry
Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry
Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Sanne Abeln
- Department
of Computer Science, Vrije Universiteit
Amsterdam, 1081 HV Amsterdam, The Netherlands
| |
Collapse
|
29
|
Hasan MM, Nabi AN, Yasmin T. Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach. J Biomol Struct Dyn 2023; 41:8002-8017. [PMID: 36166622 DOI: 10.1080/07391102.2022.2127908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/17/2022] [Indexed: 10/14/2022]
Abstract
Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, PGR, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the PGR gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the PGR gene's SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the PGR gene.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- M Mahbub Hasan
- Population Genetics Laboratory, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Ahm Nurun Nabi
- Population Genetics Laboratory, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Tahirah Yasmin
- Population Genetics Laboratory, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| |
Collapse
|
30
|
Miles AJ, Drew ED, Wallace BA. DichroIDP: a method for analyses of intrinsically disordered proteins using circular dichroism spectroscopy. Commun Biol 2023; 6:823. [PMID: 37553525 PMCID: PMC10409736 DOI: 10.1038/s42003-023-05178-2] [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: 09/09/2022] [Accepted: 07/25/2023] [Indexed: 08/10/2023] Open
Abstract
Intrinsically disordered proteins (IDPs) are comprised of significant numbers of residues that form neither helix, sheet, nor any other canonical type of secondary structure. They play important roles in a broad range of biological processes, such as molecular recognition and signalling, largely due to their chameleon-like ability to change structure from unordered when free in solution to ordered when bound to partner molecules. Circular dichroism (CD) spectroscopy is a widely-used method for characterising protein secondary structures, but analyses of IDPs using CD spectroscopy have suffered because the methods and reference datasets used for the empirical determination of secondary structures do not contain adequate representations of unordered structures. This work describes the creation, validation and testing of a standalone Windows-based application, DichroIDP, and a new reference dataset, IDP175, which is suitable for analyses of proteins containing significant amounts of disordered structure. DichroIDP enables secondary structure determinations of IDPs and proteins containing intrinsically disordered regions.
Collapse
Affiliation(s)
- Andrew J Miles
- Institute of Structural and Molecular Biology, Birkbeck University of London, London, WC1E 7HX, UK
| | - Elliot D Drew
- School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
- Zappi, London, NW1 7JN, UK
| | - B A Wallace
- Institute of Structural and Molecular Biology, Birkbeck University of London, London, WC1E 7HX, UK.
| |
Collapse
|
31
|
Xu S, Suttapitugsakul S, Tong M, Wu R. Systematic analysis of the impact of phosphorylation and O-GlcNAcylation on protein subcellular localization. Cell Rep 2023; 42:112796. [PMID: 37453062 PMCID: PMC10530397 DOI: 10.1016/j.celrep.2023.112796] [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: 05/23/2022] [Revised: 05/02/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023] Open
Abstract
The subcellular localization of proteins is critical for their functions in eukaryotic cells and is tightly correlated with protein modifications. Here, we comprehensively investigate the nuclear-cytoplasmic distributions of the phosphorylated, O-GlcNAcylated, and non-modified forms of proteins to dissect the correlation between protein distribution and modifications. Phosphorylated and O-GlcNAcylated proteins have overall higher nuclear distributions than non-modified ones. Different distributions among the phosphorylated, O-GlcNAcylated, and non-modified forms of proteins are associated with protein size, structure, and function, as well as local environment and adjacent residues around modification sites. Moreover, we perform site-mutagenesis experiments using phosphomimetic and phospho-null mutants of two proteins to validate the proteomic results. Additionally, the effects of the OGT/OGA inhibition on glycoprotein distribution are systematically investigated, and the distribution changes of glycoproteins are related to their abundance changes under the inhibitions. Systematic investigation of the relationship between protein modification and localization advances our understanding of protein functions.
Collapse
Affiliation(s)
- Senhan Xu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Suttipong Suttapitugsakul
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Ming Tong
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Ronghu Wu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA.
| |
Collapse
|
32
|
Azimi-Resketi M, Akbari M, Heydaryan S, Eftekhari A, Balali J, Shams M, Sargazi D. In silico analysis of sporozoite surface antigen 1 of Theileria annulata (TaSPAG1) for multi-epitope vaccine design against theileriosis. In Silico Pharmacol 2023; 11:16. [PMID: 37484780 PMCID: PMC10356686 DOI: 10.1007/s40203-023-00153-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/05/2023] [Indexed: 07/25/2023] Open
Abstract
Tropical theileriosis is a protozoan infection caused by Theileria annulata, which significantly affects cattle worldwide. This study was aimed to analyze the TaSPAG1 protein and design a novel multi-epitope vaccine candidate. Online tools were employed for the prediction of Physico-chemical properties, antigenicity, allergenicity, solubility, transmembrane domains and signal peptide, posttranslational modification (PTM) sites, secondary and tertiary structures as well as intrinsically disordered regions, followed by identification and screening of potential linear and conformational B-cell epitopes and those peptides having affinity to bind bovine major histocompatibility complex class I (MHC-I) molecules. Next, a multi-epitope vaccine construct was designed and analyzed. This 907-residue protein was hydrophilic (GRAVY: -0.399) and acidic (pI: 5.04) in nature, with high thermotolerance (aliphatic: 71.27). Also, 5 linear and 12 conformational B-cell epitopes along with 8 CTL epitopes were predicted for TaSPAG1. The 355-residue vaccine candidate had a MW of about 35 kDa and it was antigenic, non-allergenic, soluble and stable, which was successfully interacted with cattle MHC-I molecule and finally cloned into the pET28a(+) vector. Further wet studies are required to assess the vaccine efficacy in cattle. Supplementary Information The online version contains supplementary material available at 10.1007/s40203-023-00153-5.
Collapse
Affiliation(s)
- Mojtaba Azimi-Resketi
- Department of Parasitology and Mycology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehdi Akbari
- Department of Parasitology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Saeed Heydaryan
- Department of Internal Medicine, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Amirreza Eftekhari
- Faculty of Veterinary Medicine, Garmsar Branch, Islamic Azad University, Garmsar, Iran
| | - Javad Balali
- Doctor of Veterinary Medicine student, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
| | - Morteza Shams
- Zoonotic Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran
| | - Dariush Sargazi
- Doctorate in Veterinary Medicine, Head of Zabol Veterinary Network, Zabol, Baluchistan, Sistan Iran
| |
Collapse
|
33
|
Sansbury SE, Serebrenik YV, Lapidot T, Burslem GM, Shalem O. Pooled tagging and hydrophobic targeting of endogenous proteins for unbiased mapping of unfolded protein responses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548611. [PMID: 37503003 PMCID: PMC10370017 DOI: 10.1101/2023.07.13.548611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
System-level understanding of proteome organization and function requires methods for direct visualization and manipulation of proteins at scale. We developed an approach enabled by high-throughput gene tagging for the generation and analysis of complex cell pools with endogenously tagged proteins. Proteins are tagged with HaloTag to enable visualization or direct perturbation. Fluorescent labeling followed by in situ sequencing and deep learning-based image analysis identifies the localization pattern of each tag, providing a bird's-eye-view of cellular organization. Next, we use a hydrophobic HaloTag ligand to misfold tagged proteins, inducing spatially restricted proteotoxic stress that is read out by single cell RNA sequencing. By integrating optical and perturbation data, we map compartment-specific responses to protein misfolding, revealing inter-compartment organization and direct crosstalk, and assigning proteostasis functions to uncharacterized genes. Altogether, we present a powerful and efficient method for large-scale studies of proteome dynamics, function, and homeostasis.
Collapse
|
34
|
Danzi MC, Dohrn MF, Fazal S, Beijer D, Rebelo AP, Cintra V, Züchner S. Deep structured learning for variant prioritization in Mendelian diseases. Nat Commun 2023; 14:4167. [PMID: 37443090 PMCID: PMC10345112 DOI: 10.1038/s41467-023-39306-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/07/2023] [Indexed: 07/15/2023] Open
Abstract
Effective computer-aided or automated variant evaluations for monogenic diseases will expedite clinical diagnostic and research efforts of known and novel disease-causing genes. Here we introduce MAVERICK: a Mendelian Approach to Variant Effect pRedICtion built in Keras. MAVERICK is an ensemble of transformer-based neural networks that can classify a wide range of protein-altering single nucleotide variants (SNVs) and indels and assesses whether a variant would be pathogenic in the context of dominant or recessive inheritance. We demonstrate that MAVERICK outperforms all other major programs that assess pathogenicity in a Mendelian context. In a cohort of 644 previously solved patients with Mendelian diseases, MAVERICK ranks the causative pathogenic variant within the top five variants in over 95% of cases. Seventy-six percent of cases were solved by the top-ranked variant. MAVERICK ranks the causative pathogenic variant in hitherto novel disease genes within the first five candidate variants in 70% of cases. MAVERICK has already facilitated the identification of a novel disease gene causing a degenerative motor neuron disease. These results represent a significant step towards automated identification of causal variants in patients with Mendelian diseases.
Collapse
Affiliation(s)
- Matt C Danzi
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Maike F Dohrn
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Department of Neurology, Medical Faculty of the RWTH Aachen University, Aachen, Germany
| | - Sarah Fazal
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Danique Beijer
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Adriana P Rebelo
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Vivian Cintra
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Stephan Züchner
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.
| |
Collapse
|
35
|
Stražar M, Park J, Abelin JG, Taylor HB, Pedersen TK, Plichta DR, Brown EM, Eraslan B, Hung YM, Ortiz K, Clauser KR, Carr SA, Xavier RJ, Graham DB. HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery. Immunity 2023; 56:1681-1698.e13. [PMID: 37301199 PMCID: PMC10519123 DOI: 10.1016/j.immuni.2023.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/08/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023]
Abstract
CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.
Collapse
Affiliation(s)
- Martin Stražar
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jihye Park
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Hannah B Taylor
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thomas K Pedersen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Eric M Brown
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Basak Eraslan
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yuan-Mao Hung
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Kayla Ortiz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Daniel B Graham
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| |
Collapse
|
36
|
Tovar-Ramírez ME, Schuth N, Rodríguez O, Kroll T, Saab-Rincon G, Costas M, Lampi K, Quintanar L. ATCUN-like Copper Site in βB2-Crystallin Plays a Protective Role in Cataract-Associated Aggregation. Inorg Chem 2023; 62:10592-10604. [PMID: 37379524 PMCID: PMC11156493 DOI: 10.1021/acs.inorgchem.3c00794] [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] [Indexed: 06/30/2023]
Abstract
Cataract is the leading cause of blindness worldwide, and it is caused by crystallin damage and aggregation. Senile cataractous lenses have relatively high levels of metals, while some metal ions can directly induce the aggregation of human γ-crystallins. Here, we evaluated the impact of divalent metal ions in the aggregation of human βB2-crystallin, one of the most abundant crystallins in the lens. Turbidity assays showed that Pb2+, Hg2+, Cu2+, and Zn2+ ions induce the aggregation of βB2-crystallin. Metal-induced aggregation is partially reverted by a chelating agent, indicating the formation of metal-bridged species. Our study focused on the mechanism of copper-induced aggregation of βB2-crystallin, finding that it involves metal-bridging, disulfide-bridging, and loss of protein stability. Circular dichroism and electron paramagnetic resonance (EPR) revealed the presence of at least three Cu2+ binding sites in βB2-crystallin, one of them with spectroscopic features typical for Cu2+ bound to an amino-terminal copper and nickel (ATCUN) binding motif, which is found in Cu transport proteins. The ATCUN-like Cu binding site is located at the unstructured N-terminus of βB2-crystallin, and it could be modeled by a peptide with the first six residues in the protein sequence (NH2-ASDHQF-). Isothermal titration calorimetry indicates a nanomolar Cu2+ binding affinity for the ATCUN-like site. An N-truncated form of βB2-crystallin is more susceptible to Cu-induced aggregation and is less thermally stable, indicating a protective role for the ATCUN-like site. EPR and X-ray absorption spectroscopy studies reveal the presence of a copper redox active site in βB2-crystallin that is associated with metal-induced aggregation and formation of disulfide-bridged oligomers. Our study demonstrates metal-induced aggregation of βB2-crystallin and the presence of putative copper binding sites in the protein. Whether the copper-transport ATCUN-like site in βB2-crystallin plays a functional/protective role or constitutes a vestige from its evolution as a lens structural protein remains to be elucidated.
Collapse
Affiliation(s)
- Martin E. Tovar-Ramírez
- Department of Chemistry, Centro de Investigación y de Estudios Avanzados (Cinvestav), Mexico City, 07360, Mexico
| | - Nils Schuth
- Department of Chemistry, Centro de Investigación y de Estudios Avanzados (Cinvestav), Mexico City, 07360, Mexico
| | - Oscar Rodríguez
- Facultad de Química, Universidad Nacional Autónoma de México (UNAM), Mexico City, 04510, Mexico
| | - Thomas Kroll
- Stanford Synchrotron Radiation Lightsource (SSRL), SLAC National Accelerator Laboratory, Menlo Park, 94025, CA, USA
| | - Gloria Saab-Rincon
- Department of Biocatalysis and Cellular Engineering, Instituto de Biotecnología, Universidad Nacional Autónoma de Mexico, Cuernavaca, Morelos, 62210, Mexico
| | - Miguel Costas
- Facultad de Química, Universidad Nacional Autónoma de México (UNAM), Mexico City, 04510, Mexico
| | - Kirsten Lampi
- Integrative Biosciences, Oregon Health & Science University, Portland, Oregon, 97239, United States
| | - Liliana Quintanar
- Department of Chemistry, Centro de Investigación y de Estudios Avanzados (Cinvestav), Mexico City, 07360, Mexico
| |
Collapse
|
37
|
Ahammad I, Jamal TB, Bhattacharjee A, Chowdhury ZM, Rahman S, Hassan MR, Hossain MU, Das KC, Keya CA, Salimullah M. Impact of highly deleterious non-synonymous polymorphisms on GRIN2A protein's structure and function. PLoS One 2023; 18:e0286917. [PMID: 37319252 PMCID: PMC10270607 DOI: 10.1371/journal.pone.0286917] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/25/2023] [Indexed: 06/17/2023] Open
Abstract
GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases.
Collapse
Affiliation(s)
- Ishtiaque Ahammad
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh
| | - Tabassum Binte Jamal
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh
| | - Arittra Bhattacharjee
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh
| | - Zeshan Mahmud Chowdhury
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh
| | - Suparna Rahman
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, Bangladesh
| | - Md Rakibul Hassan
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, Bangladesh
| | - Mohammad Uzzal Hossain
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh
| | - Keshob Chandra Das
- Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh
| | - Chaman Ara Keya
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, Bangladesh
| | - Md Salimullah
- Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh
| |
Collapse
|
38
|
Meng Q, Guo F, Tang J. Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model. Brief Bioinform 2023:bbad217. [PMID: 37321965 DOI: 10.1093/bib/bbad217] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/18/2023] [Accepted: 05/21/2023] [Indexed: 06/17/2023] Open
Abstract
In recent years, protein structure problems have become a hotspot for understanding protein folding and function mechanisms. It has been observed that most of the protein structure works rely on and benefit from co-evolutionary information obtained by multiple sequence alignment (MSA). As an example, AlphaFold2 (AF2) is a typical MSA-based protein structure tool which is famous for its high accuracy. As a consequence, these MSA-based methods are limited by the quality of the MSAs. Especially for orphan proteins that have no homologous sequence, AlphaFold2 performs unsatisfactorily as MSA depth decreases, which may pose a barrier to its widespread application in protein mutation and design problems in which there are no rich homologous sequences and rapid prediction is needed. In this paper, we constructed two standard datasets for orphan and de novo proteins which have insufficient/none homology information, called Orphan62 and Design204, respectively, to fairly evaluate the performance of the various methods in this case. Then, depending on whether or not utilizing scarce MSA information, we summarized two approaches, MSA-enhanced and MSA-free methods, to effectively solve the issue without sufficient MSAs. MSA-enhanced model aims to improve poor MSA quality from the data source by knowledge distillation and generation models. MSA-free model directly learns the relationship between residues on enormous protein sequences from pre-trained models, bypassing the step of extracting the residue pair representation from MSA. Next, we evaluated the performance of four MSA-free methods (trRosettaX-Single, TRFold, ESMFold and ProtT5) and MSA-enhanced (Bagging MSA) method compared with a traditional MSA-based method AlphaFold2, in two protein structure-related prediction tasks, respectively. Comparison analyses show that trRosettaX-Single and ESMFold which belong to MSA-free method can achieve fast prediction ($\sim\! 40$s) and comparable performance compared with AF2 in tertiary structure prediction, especially for short peptides, $\alpha $-helical segments and targets with few homologous sequences. Bagging MSA utilizing MSA enhancement improves the accuracy of our trained base model which is an MSA-based method when poor homology information exists in secondary structure prediction. Our study provides biologists an insight of how to select rapid and appropriate prediction tools for enzyme engineering and peptide drug development. CONTACT guofei@csu.edu.cn, jj.tang@siat.ac.cn.
Collapse
Affiliation(s)
- Qiaozhen Meng
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| |
Collapse
|
39
|
Spät P, Krauspe V, Hess WR, Maček B, Nalpas N. Deep Proteogenomics of a Photosynthetic Cyanobacterium. J Proteome Res 2023; 22:1969-1983. [PMID: 37146978 PMCID: PMC10243305 DOI: 10.1021/acs.jproteome.3c00065] [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: 02/02/2023] [Indexed: 05/07/2023]
Abstract
Cyanobacteria, the evolutionary ancestors of plant chloroplasts, contribute substantially to the Earth's biogeochemical cycles and are of great interest for a sustainable economy. Knowledge of protein expression is the key to understanding cyanobacterial metabolism; however, proteome studies in cyanobacteria are limited and cover only a fraction of the theoretical proteome. Here, we performed a comprehensive proteogenomic analysis of the model cyanobacterium Synechocystis sp. PCC 6803 to characterize the expressed (phospho)proteome, re-annotate known and discover novel open reading frames (ORFs). By mapping extensive shotgun mass spectrometry proteomics data onto a six-frame translation of the Synechocystis genome, we refined the genomic annotation of 64 ORFs, including eight completely novel ORFs. Our study presents the largest reported (phospho)proteome dataset for a unicellular cyanobacterium, covering the expression of about 80% of the theoretical proteome under various cultivation conditions, such as nitrogen or carbon limitation. We report 568 phosphorylated S/T/Y sites that are present on numerous regulatory proteins, including the transcriptional regulators cyAbrB1 and cyAbrB2. We also catalogue the proteins that have never been detected under laboratory conditions and found that a large portion of them is plasmid-encoded. This dataset will serve as a resource, providing dedicated information on growth condition-dependent protein expression and phosphorylation.
Collapse
Affiliation(s)
- Philipp Spät
- Quantitative
Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany
| | - Vanessa Krauspe
- Genetics
& Experimental Bioinformatics, Institute of Biology III, University of Freiburg, Schänzlestraße 1, 79104 Freiburg im Breisgau, Germany
| | - Wolfgang R. Hess
- Genetics
& Experimental Bioinformatics, Institute of Biology III, University of Freiburg, Schänzlestraße 1, 79104 Freiburg im Breisgau, Germany
| | - Boris Maček
- Quantitative
Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany
| | - Nicolas Nalpas
- Quantitative
Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany
| |
Collapse
|
40
|
Fabo T, Khavari P. Functional characterization of human genomic variation linked to polygenic diseases. Trends Genet 2023; 39:462-490. [PMID: 36997428 PMCID: PMC11025698 DOI: 10.1016/j.tig.2023.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/30/2023]
Abstract
The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.
Collapse
Affiliation(s)
- Tania Fabo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Paul Khavari
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA; Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA.
| |
Collapse
|
41
|
Bhatnagar P, Bajpai P, Shrinet J, Kaja MK, Chandele A, Sitaraman R. Prediction of human protein interactome of dengue virus non-structural protein 5 (NS5) and its downstream immunological implications. 3 Biotech 2023; 13:180. [PMID: 37193327 PMCID: PMC10182223 DOI: 10.1007/s13205-023-03569-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 04/19/2023] [Indexed: 05/18/2023] Open
Abstract
The non-structural protein 5 (NS5) is the most conserved protein among flaviviruses, a family that includes the dengue virus. It functions both as an RNA-dependent RNA polymerase and an RNA-methyltransferase and is therefore essential for the replication of viral RNA. The discovery that dengue virus NS5 protein (DENV-NS5) can also localize to the nucleus has resulted in renewed interest in its potential roles at the host-virus interface. In this study, we have used two complementary computational approaches in parallel - one based on linear motifs (ELM) and another based on tertiary structure of the protein (DALI) - to predict the host proteins that DENV-NS5 might interact with. Of the 42 human proteins predicted by both these methods, 34 are novel. Pathway analysis of these 42 human proteins shows that they are involved in key host cellular processes related to cell cycle regulation, proliferation, protein degradation, apoptosis, and immune responses. A focused analysis of transcription factors that directly interact with the predicted DENV-NS5 interacting proteins was performed, followed by the identification of downstream genes that are differentially expressed after dengue infection using previously published RNA-seq data. Our study provides unique insights into the DENV-NS5 interaction network and delineates mechanisms whereby DENV-NS5 could impact the host-virus interface. The novel interactors identified in this study could be potentially targeted by NS5 to modulate the host cellular environment in general, and the immune response in particular, thereby extending the role of DENV-NS5 beyond its known enzymatic functions. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-023-03569-0.
Collapse
Affiliation(s)
- Priya Bhatnagar
- Department of Biotechnology, TERI School of Advanced Studies, New Delhi, India
- ICGEB-Emory Vaccine Centre, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Prashant Bajpai
- ICGEB-Emory Vaccine Centre, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Jatin Shrinet
- Department of Biological Science, Florida State University, Tallahassee, FL 32306 USA
| | - Murali Krishna Kaja
- ICGEB-Emory Vaccine Centre, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
- Department of Pediatrics and Emory Vaccine Centre, Emory University School of Medicine, Atlanta, GA USA
| | - Anmol Chandele
- ICGEB-Emory Vaccine Centre, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | | |
Collapse
|
42
|
Kim Y, Kwon J. AttSec: protein secondary structure prediction by capturing local patterns from attention map. BMC Bioinformatics 2023; 24:183. [PMID: 37142993 PMCID: PMC10161504 DOI: 10.1186/s12859-023-05310-3] [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: 01/01/2023] [Accepted: 04/27/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Protein secondary structures that link simple 1D sequences to complex 3D structures can be used as good features for describing the local properties of protein, but also can serve as key features for predicting the complex 3D structures of protein. Thus, it is very important to accurately predict the secondary structure of the protein, which contains a local structural property assigned by the pattern of hydrogen bonds formed between amino acids. In this study, we accurately predict protein secondary structure by capturing the local patterns of protein. For this objective, we present a novel prediction model, AttSec, based on transformer architecture. In particular, AttSec extracts self-attention maps corresponding to pairwise features between amino acid embeddings and passes them through 2D convolution blocks to capture local patterns. In addition, instead of using additional evolutionary information, it uses protein embedding as an input, which is generated by a language model. RESULTS For the ProteinNet DSSP8 dataset, our model showed 11.8% better performance on the entire evaluation datasets compared with other no-evolutionary-information-based models. For the NetSurfP-2.0 DSSP8 dataset, it showed 1.2% better performance on average. There was an average performance improvement of 9.0% for the ProteinNet DSSP3 dataset and an average of 0.7% for the NetSurfP-2.0 DSSP3 dataset. CONCLUSION We accurately predict protein secondary structure by capturing the local patterns of protein. For this objective, we present a novel prediction model, AttSec, based on transformer architecture. Although there was no dramatic accuracy improvement compared with other models, the improvement on DSSP8 was greater than that on DSSP3. This result implies that using our proposed pairwise feature could have a remarkable effect for several challenging tasks that require finely subdivided classification. Github package URL is https://github.com/youjin-DDAI/AttSec .
Collapse
Affiliation(s)
- Youjin Kim
- Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea
- LG AI Research, Seoul, Republic of Korea
| | - Junseok Kwon
- Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea.
| |
Collapse
|
43
|
Senba H, Nishikawa A, Kimura Y, Tanaka S, Matsumoto JI, Doi M, Takenaka S. Improvement in salt-tolerance of Aspergillus oryzae γ-glutamyl transpeptidase via protein chimerization with Aspergillus sydowii homolog. Enzyme Microb Technol 2023; 167:110240. [PMID: 37084614 DOI: 10.1016/j.enzmictec.2023.110240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/09/2023] [Accepted: 04/10/2023] [Indexed: 04/23/2023]
Abstract
γ-Glutamyl transpeptidase is one of the key enzymes involved in glutamate production during high-salt fermentation of soy sauce and miso by koji mold, Aspergillus oryzae. However, the activity of γ-glutamyl transpeptidase from A. oryzae (AOggtA) is markedly reduced in the presence of NaCl, thus classifying it as a non-salt-tolerant enzyme. In contrast, the homologous protein from the xerophilic mold, A. sydowii (ASggtA) maintains its activity under high-salt conditions. Therefore, in this study, a chimeric enzyme, ASAOggtA, was designed and engineered to improve salt-tolerance in AOggtA by swapping the N-terminal region, based on sequence and structure comparisons between salt-tolerant ASggtA and non-salt-tolerant AOggtA. The parental AOggtA and ASggtA and their chimera, ASAOggtA, were heterologously expressed in A. oryzae and purified. The chimeric enzyme inherited the superior activity and stability from each of the two parent enzymes. ASAOggtA showed > 2-fold greater tolerance than AOggtA in the presence of 18% NaCl. In addition, the chimera showed a broader range of pH stability and greater thermostability than ASggtA. AOggtA and ASAOggtA were sy over the range pH 3.0 to pH 10.5. Thermal stability was found to be in the order AOggtA (57.5 °C, t1/2 = 32.5 min) > ASAOggtA (55 °C, t1/2 = 20.5 min) > ASggtA (50 °C, t1/2 = 12.5 min). The catalytic and structural characteristics indicated that non-salt-tolerant AOggtA would not undergo irreversible structural changes in the presence of NaCl, but rather a temporary conformational change, which might result in reducing the substrate binding and catalytic activity, on the basis of kinetic properties. In addition, the chimeric enzyme showed hydrolytic activity toward L-glutamine that was as high as that of AOggtA. The newly-designed chimeric ASAOggtA might have potential applications in high-salt fermentation, such as miso and shoyu, to increase the content of the umami-flavor amino acid, L-glutamate.
Collapse
Affiliation(s)
- Hironori Senba
- Division of Agrobioscience, Graduate School of Agricultural Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan; Ozeki Corp, Gen Res Lab, 4-9 Imazu, Nishinomiya, Hyogo 6638227, Japan
| | - Arisa Nishikawa
- Division of Agrobioscience, Graduate School of Agricultural Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Yukihiro Kimura
- Division of Agrobioscience, Graduate School of Agricultural Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Shinichi Tanaka
- Marutomo Co., Ltd, 1696 Kominato, Iyo, Ehime 799-3192, Japan
| | | | - Mikiharu Doi
- Marutomo Co., Ltd, 1696 Kominato, Iyo, Ehime 799-3192, Japan
| | - Shinji Takenaka
- Division of Agrobioscience, Graduate School of Agricultural Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan.
| |
Collapse
|
44
|
Ashour DJ, Durney CH, Planelles-Herrero VJ, Stevens TJ, Feng JJ, Röper K. Zasp52 strengthens whole embryo tissue integrity through supracellular actomyosin networks. Development 2023; 150:dev201238. [PMID: 36897564 PMCID: PMC10112930 DOI: 10.1242/dev.201238] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 02/28/2023] [Indexed: 03/11/2023]
Abstract
During morphogenesis, large-scale changes of tissue primordia are coordinated across an embryo. In Drosophila, several tissue primordia and embryonic regions are bordered or encircled by supracellular actomyosin cables, junctional actomyosin enrichments networked between many neighbouring cells. We show that the single Drosophila Alp/Enigma-family protein Zasp52, which is most prominently found in Z-discs of muscles, is a component of many supracellular actomyosin structures during embryogenesis, including the ventral midline and the boundary of the salivary gland placode. We reveal that Zasp52 contains within its central coiled-coil region a type of actin-binding motif usually found in CapZbeta proteins, and this domain displays actin-binding activity. Using endogenously-tagged lines, we identify that Zasp52 interacts with junctional components, including APC2, Polychaetoid and Sidekick, and actomyosin regulators. Analysis of zasp52 mutant embryos reveals that the severity of the embryonic defects observed scales inversely with the amount of functional protein left. Large tissue deformations occur where actomyosin cables are found during embryogenesis, and in vivo and in silico analyses suggest a model whereby supracellular Zasp52-containing cables aid to insulate morphogenetic changes from one another.
Collapse
Affiliation(s)
- Dina J. Ashour
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
| | - Clinton H. Durney
- Department of Mathematics, University of British Columbia, Vancouver, V6T 1Z2Canada
| | | | - Tim J. Stevens
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
| | - James J. Feng
- Department of Mathematics, University of British Columbia, Vancouver, V6T 1Z2Canada
- Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, V6T 1Z3Canada
| | - Katja Röper
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
| |
Collapse
|
45
|
Ibtehaz N, Sourav SMSH, Bayzid MS, Rahman MS. Align-gram: Rethinking the Skip-gram Model for Protein Sequence Analysis. Protein J 2023; 42:135-146. [PMID: 36977849 DOI: 10.1007/s10930-023-10096-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2023] [Indexed: 03/29/2023]
Abstract
The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the 'language of life', has been analyzed for a multitude of applications and inferences. Owing to the rapid development of deep learning, in recent years there have been a number of breakthroughs in the domain of Natural Language Processing. Since these methods are capable of performing different tasks when trained with a sufficient amount of data, off-the-shelf models are used to perform various biological applications. In this study, we investigated the applicability of the popular Skip-gram model for protein sequence analysis and made an attempt to incorporate some biological insights into it. We propose a novel k-mer embedding scheme, Align-gram, which is capable of mapping the similar k-mers close to each other in a vector space. Furthermore, we experiment with other sequence-based protein representations and observe that the embeddings derived from Align-gram aids modeling and training deep learning models better. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis.
Collapse
|
46
|
Dps-dependent in vivo mutation enhances long-term host adaptation in Vibrio cholerae. PLoS Pathog 2023; 19:e1011250. [PMID: 36928244 PMCID: PMC10104298 DOI: 10.1371/journal.ppat.1011250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 04/14/2023] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
As one of the most successful pathogenic organisms, Vibrio cholerae (V. cholerae) has evolved sophisticated regulatory mechanisms to overcome host stress. During long-term colonization by V. cholerae in adult mice, many spontaneous nonmotile mutants (approximately 10% at the fifth day post-infection) were identified. These mutations occurred primarily in conserved regions of the flagellar regulator genes flrA, flrC, and rpoN, as shown by Sanger and next-generation sequencing, and significantly increased fitness during colonization in adult mice. Intriguingly, instead of key genes in DNA repair systems (mutS, nfo, xthA, uvrA) or ROS and RNS scavenging systems (katG, prxA, hmpA), which are generally thought to be associated with bacterial mutagenesis, we found that deletion of the cyclin gene dps significantly increased the mutation rate (up to 53% at the fifth day post-infection) in V. cholerae. We further determined that the dpsD65A and dpsF46E point mutants showed a similar mutagenesis profile as the Δdps mutant during long-term colonization in mice, which strongly indicated that the antioxidative function of Dps directly contributes to the development of V. cholerae nonmotile mutants. Methionine metabolism pathway may be one of the mechanism for ΔflrA, ΔflrC and ΔrpoN mutant increased colonization in adult mice. Our results revealed a new phenotype in which V. cholerae fitness increases in the host gut via spontaneous production nonmotile mutants regulated by cyclin Dps, which may represent a novel adaptation strategy for directed evolution of pathogens in the host.
Collapse
|
47
|
Bhattacharjee A, Pranto SMAM, Ahammad I, Chowdhury ZM, Juliana FM, Das KC, Keya CA, Salimullah M. High risk genetic variants of human insulin receptor substrate 1(IRS1) infer structural instability and functional interference. J Biomol Struct Dyn 2023; 41:15150-15164. [PMID: 36907599 DOI: 10.1080/07391102.2023.2187232] [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: 12/07/2022] [Accepted: 02/23/2023] [Indexed: 03/14/2023]
Abstract
Insulin receptor substrate 1(IRS1) is a signaling adapter protein encoded by the IRS1 gene. This protein delivers signals from insulin and insulin-like growth factor-1(IGF-1) receptors to the phosphatidylinositol 3-kinases (P13K)/protein kinase B (Akt) and Extracellular signal-regulated kinases (Erk) - Mitogen-activated protein (MAP) kinase pathways, which regulate particular cellular processes. Mutations in this gene have been linked to type 2 diabetes mellitus, a heightened risk of insulin resistance, and an increased likelihood of developing a number of different malignancies. The structure and function of IRS1 could be severely compromised as a result of single nucleotide polymorphism (SNP) type genetic variants. In this study, we focused on identification of the most harmful non-synonymous SNPs (nsSNPs) of the IRS1 gene as well as prediction of their structural and functional consequences. Six different algorithms made the initial prediction that 59 of the 1142 IRS1 nsSNPs would have a negative impact on the protein structure. In-depth evaluations detected 26 nsSNPs located inside the functional domains of IRS1. Following that, 16 nsSNPs were identified as more harmful based on conservation profile, hydrophobic interaction, surface accessibility, homology modelling, and inter-atomic interactions. Following an in-depth analysis of protein stability, M249T (rs373826433), I223T (rs1939785175) and V204G (rs1574667052) were identified as three most deleterious SNPs and were subjected to molecular dynamics simulation for further insights. These findings will help us understand the implications for disease susceptibility, cancer progression, and the efficacy of therapeutic development against IRS1 gene mutants.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
| | - S M Al Muied Pranto
- Department of Biochemistry & Molecular Biology, Jahangirnagar University, Savar, Bangladesh
| | - Ishtiaque Ahammad
- Bioinformatics Division, National Institute of Biotechnology, Savar, Bangladesh
| | | | - Farha Matin Juliana
- Department of Biochemistry & Molecular Biology, Jahangirnagar University, Savar, Bangladesh
| | - Keshob Chandra Das
- Molecular Biotechnology Division, National Institute of Biotechnology, Savar, Bangladesh
| | - Chaman Ara Keya
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Bangladesh
| | - Md Salimullah
- Molecular Biotechnology Division, National Institute of Biotechnology, Savar, Bangladesh
| |
Collapse
|
48
|
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.
Collapse
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
| |
Collapse
|
49
|
Gormez Y, Aydin Z. IGPRED-MultiTask: A Deep Learning Model to Predict Protein Secondary Structure, Torsion Angles and Solvent Accessibility. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1104-1113. [PMID: 35849663 DOI: 10.1109/tcbb.2022.3191395] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Protein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein structure. In this study, IGPRED-Multitask, a deep learning model with multi task learning architecture based on deep inception network, graph convolutional network and a bidirectional long short-term memory is proposed. Moreover, hyper-parameters of the model are fine-tuned using Bayesian optimization, which is faster and more effective than grid search. The same benchmark test data sets as in the OPUS-TASS paper including TEST2016, TEST2018, CASP12, CASP13, CASPFM, HARD68, CAMEO93, CAMEO93_HARD, as well as the train and validation sets, are used for fair comparison with the literature. Statistically significant improvements are observed in secondary structure prediction on 4 datasets, in phi angle prediction on 2 datasets and in psi angel prediction on 3 datasets compared to the state-of-the-art methods. For solvent accessibility prediction, TEST2016 and TEST2018 datasets are used only to assess the performance of the proposed model.
Collapse
|
50
|
Leenheer D, Moreno AB, Paranjape K, Murray S, Jarraud S, Ginevra C, Guy L. Rapid adaptations of Legionella pneumophila to the human host. Microb Genom 2023; 9. [PMID: 36947445 PMCID: PMC10132064 DOI: 10.1099/mgen.0.000958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023] Open
Abstract
Legionella pneumophila are host-adapted bacteria that infect and reproduce primarily in amoeboid protists. Using similar infection mechanisms, they infect human macrophages, and cause Legionnaires' disease, an atypical pneumonia, and the milder Pontiac fever. We hypothesized that, despite the similarities in infection mechanisms, the hosts are different enough that there exist high-selective value mutations that would dramatically increase the fitness of Legionella inside the human host. By comparing a large number of isolates from independent infections, we identified two genes, mutated in three unrelated patients, despite the short duration of the incubation period (2-14 days). One is a gene coding for an outer membrane protein (OMP) belonging to the OmpP1/FadL family. The other is a gene coding for an EAL-domain-containing protein involved in cyclic-di-GMP regulation, which in turn modulates flagellar activity. The clinical strain, carrying the mutated EAL-domain-containing homologue, grows faster in macrophages than the wild-type strain, and thus appears to be better adapted to the human host. As human-to-human transmission is very rare, fixation of these mutations into the population and spread into the environment is unlikely. Therefore, parallel evolution - here mutations in the same genes observed in independent human infections - could point to adaptations to the accidental human host. These results suggest that despite the ability of L. pneumophila to infect, replicate in and exit from macrophages, its human-specific adaptations are unlikely to be fixed in the population.
Collapse
Affiliation(s)
- Daniël Leenheer
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Ph.D. Program in Human Biology, School of Integrative and Global Majors, University of Tsukuba, Tsukuba, Japan
| | - Anaísa B Moreno
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Kiran Paranjape
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Susan Murray
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Sophie Jarraud
- French National Reference Center of Legionella, Institute of Infectious Agents, Hospices Civils de Lyon, Lyon, France
- CIRI, Centre International de Recherche en Infectiologie, Legionella Pathogenesis Team, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | - Christophe Ginevra
- French National Reference Center of Legionella, Institute of Infectious Agents, Hospices Civils de Lyon, Lyon, France
- CIRI, Centre International de Recherche en Infectiologie, Legionella Pathogenesis Team, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | - Lionel Guy
- Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| |
Collapse
|