1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Structural implications of BK polyomavirus sequence variations in the major viral capsid protein Vp1 and large T-antigen: a computational study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          ABSTRACT

          BK polyomavirus (BKPyV) is a double-stranded DNA virus causing nephropathy, hemorrhagic cystitis, and urothelial cancer in transplant patients. The BKPyV-encoded capsid protein Vp1 and large T-antigen (LTag) are key targets of neutralizing antibodies and cytotoxic T-cells, respectively. Our single-center data suggested that variability in Vp1 and LTag may contribute to failing BKPyV-specific immune control and impact vaccine design. We, therefore, analyzed all available entries in GenBank (1516 VP1; 742 LTAG) and explored potential structural effects using computational approaches. BKPyV-genotype (gt)1 was found in 71.18% of entries, followed by BKPyV-gt4 (19.26%), BKPyV-gt2 (8.11%), and BKPyV-gt3 (1.45%), but rates differed according to country and specimen type. Vp1-mutations matched a serotype different than the assigned one or were serotype-independent in 43%, 18% affected more than one amino acid. Notable Vp1-mutations altered antibody-binding domains, interactions with sialic acid receptors, or were predicted to change conformation. LTag-sequences were more conserved, with only 16 mutations detectable in more than one entry and without significant effects on LTag-structure or interaction domains. However, LTag changes were predicted to affect HLA-class I presentation of immunodominant 9mers to cytotoxic T-cells. These global data strengthen single center observations and specifically our earlier findings revealing mutant 9mer epitopes conferring immune escape from HLA-I cytotoxic T cells. We conclude that variability of BKPyV-Vp1 and LTag may have important implications for diagnostic assays assessing BKPyV-specific immune control and for vaccine design.

          IMPORTANCE

          Type and rate of amino acid variations in BKPyV may provide important insights into BKPyV diversity in human populations and an important step toward defining determinants of BKPyV-specific immunity needed to protect vulnerable patients from BKPyV diseases. Our analysis of BKPyV sequences obtained from human specimens reveals an unexpectedly high genetic variability for this double-stranded DNA virus that strongly relies on host cell DNA replication machinery with its proof reading and error correction mechanisms. BKPyV variability and immune escape should be taken into account when designing further approaches to antivirals, monoclonal antibodies, and vaccines for patients at risk of BKPyV diseases.

          Related collections

          Most cited references58

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Basic local alignment search tool.

            A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review and editing
                Role: Formal analysisRole: MethodologyRole: ValidationRole: Writing – review and editing
                Role: Data curationRole: MethodologyRole: ResourcesRole: Writing – review and editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: Project administration
                Role: ConceptualizationRole: SupervisionRole: ValidationRole: Writing – review and editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – review and editing
                Role: ConceptualizationRole: ValidationRole: VisualizationRole: Writing – review and editing
                Role: ConceptualizationRole: Data curationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review and editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – review and editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review and editing
                Role: Editor
                Journal
                mSphere
                mSphere
                msphere
                mSphere
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5042
                April 2024
                19 March 2024
                19 March 2024
                : 9
                : 4
                : e00799-23
                Affiliations
                [1 ]Biozentrum, University of Basel; , Basel, Switzerland
                [2 ]SIB Swiss Institute of Bioinformatics; , Basel, Switzerland
                [3 ]Transplantation & Clinical Virology, Department of Biomedicine, Medical Faculty, University of Basel; , Basel, Switzerland
                [4 ]Clinical Virology, Laboratory Medicine, Department Theragnostic, University Hospital Basel; , Basel, Switzerland
                [5 ]Infectious Diseases & Hospital Epidemiology, Department Acute Medicine, University Hospital Basel; , Basel, Switzerland
                University of Zurich; , Zurich, Switzerland
                Author notes
                Address correspondence to Hans H. Hirsch, hans.hirsch@ 123456unibas.ch

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-1698-4556
                https://orcid.org/0009-0002-2577-1808
                https://orcid.org/0000-0002-5654-9356
                https://orcid.org/0000-0003-4081-9398
                https://orcid.org/0009-0008-5393-4807
                https://orcid.org/0000-0003-0969-8153
                https://orcid.org/0000-0003-2715-335X
                https://orcid.org/0000-0003-0883-0423
                Article
                00799-23 msphere.00799-23
                10.1128/msphere.00799-23
                11036806
                38501831
                61fa17bf-d1a7-4e27-add5-2f4a8be68df6
                Copyright © 2024 Durairaj et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 20 December 2023
                : 21 February 2024
                Page count
                supplementary-material: 4, authors: 10, Figures: 7, Tables: 2, References: 58, Pages: 22, Words: 11328
                Funding
                Funded by: Swiss National Science Foundation;
                Award ID: 310030_212589
                Award Recipient :
                Funded by: University of Basel;
                Award ID: MM2109
                Award Recipient :
                Funded by: Moderna (Global Fellowship);
                Award ID: 310030_212589
                Award Recipient :
                Categories
                Research Article
                computational-biology, Computational Biology
                Custom metadata
                April 2024

                polyomavirus,bk virus,serotype,genotype,variant,mutant,structure,prediction,vp1, large t antigen,immune escape,vaccine

                Comments

                Comment on this article