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Sali, A.

Publications and source records attributed to Sali, A..

4 recordsLinked to original sources

Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition

Accurate predictions of T-cell epitopes would be useful for designing vaccines, immunotherapies for cancer and autoimmune diseases, and improved protein therapies. The humoral immune response involves uptake of antigens by antigen presenting cells (APCs), APC processing and presentation of peptides on MHC class II (pMHCII), and T-cell receptor (TCR) recognition of pMHCII complexes. Most in silico methods predict only peptide-MHCII binding, resulting in significant over-prediction of CD4 T-cell epitopes. We present a method, ITCell, for prediction of T-cell epitopes within an input protein antigen sequence for given MHCII and TCR sequences. The method integrates information about three stages of the immune response pathway: antigen cleavage, MHCII presentation, and TCR recognition. First, antigen cleavage sites are predicted based on the cleavage profiles of cathepsins S, B, and H. Second, for each 12-mer peptide in the antigen sequence we predict whether it will bind to a given MHCII, based on the scores of modeled peptide-MHCII complexes. Third, we predict whether or not any of the top scoring peptide-MHCII complexes can bind to a given TCR, based on the scores of modeled ternary peptide-MHCII-TCR complexes and the distribution of predicted cleavage sites. Our benchmarks consist of epitope predictions generated by this algorithm, checked against 20 peptide-MHCII-TCR crystal structures, as well as epitope predictions for four peptide-MHCII-TCR complexes with known epitopes and TCR sequences but without crystal structures. ITCell successfully identified the correct epitopes as one of the 20 top scoring peptides for 22 of 24 benchmark cases. To validate the method using a clinically relevant application, we utilized five factor VIII-specific TCR sequences from hemophilia A subjects who developed an immune response to factor VIII replacement therapy. The known HLA-DR1-restricted factor VIII epitope was among the six top-scoring factor VIII peptides predicted by ITCall to bind HLA-DR1 and all five TCRs. Our integrative approach is more accurate than current single-stage epitope prediction algorithms applied to the same benchmarks. It is freely available as a web server (http://salilab.org/itcell).\n\nAuthor summaryKnowledge of T-cell epitopes is useful for designing vaccines, improving cancer immunotherapy, studying autoimmune diseases, and engineering protein replacement therapies. Unfortunately, experimental methods for identification of T-cell epitopes are slow, expensive, and not always applicable. Thus, a more accurate computational method for prediction of T-cell epitopes needs to be developed. While the T-cell response to extracellular antigens proceeds through multiple stages, current computational methods rely only on the prediction of peptide binding affinity to an MHCII receptor on antigen presenting cells, resulting in a relatively high number of false-positive predictions of T-cell epitopes within protein antigens. We developed an integrative approach to predict T-cell epitopes that computationally combines information from three stages of the humoral immune response pathway: antigen cleavage, MHCII presentation, and TCR recognition, resulting in an increased accuracy of epitope predictions. This method was applied to predict epitopes within blood coagulation factor VIII (FVIII) that were recognized by TCRs from hemophilia A subjects who developed an anti-FVIII antibody response. The correct epitope was predicted after modeling all possible 12-mer FVIII peptides bound in ternary complexes with the relevant MHCII (HLA-DR1) and each of five experimentally determined FVIII-specific TCR sequences.

bioinformatics

Spc110 N-Terminal Domains Act Independently to Mediate Stable γ-Tubulin Small Complex Binding and γ-Tubulin Ring Complex Assembly

Microtubule (MT) nucleation in vivo is regulated by the {gamma}-tubulin ring complex ({gamma}TuRC), an approximately 2-megadalton complex conserved from yeast to humans. In Saccharomyces cerevisiae, {gamma}TuRC assembly is a key point of regulation over the MT cytoskeleton. Budding yeast {gamma}TuRC is composed of seven {gamma}-tubulin small complex ({gamma}TuSC) subassemblies which associate helically to form a template from which microtubules grow. This assembly process requires higher-order oligomers of the coiled-coil protein Spc110 to bind multiple {gamma}TuSCs, thereby stabilizing the otherwise low-affinity interface between {gamma}TuSCs. While Spc110 oligomerization is critical, its N-terminal domain (NTD) also plays a role that is poorly understood both functionally and structurally. In this work, we sought a mechanistic understanding of Spc110 NTD using a combination of structural and biochemical analyses. Through crosslinking-mass spectrometry (XL-MS), we determined that a segment of Spc110 coiled-coil is a major point of contact with {gamma}TuSC. We determined the structure of this coiled-coil segment by X-ray crystallography and used it in combination with our XL-MS dataset to generate an integrative structural model of the {gamma}TuSC-Spc110 complex. This structural model, in combination with biochemical analyses of Spc110 heterodimers lacking one NTD, suggests that the two NTDs within an Spc110 dimer act independently, one stabilizing association between Spc110 and {gamma}TuSC and the other stabilizing the interface between adjacent {gamma}TuSCs.

biochemistry

Predictions of Protein-Protein Interactions in Schistosoma Mansoni

BackgroundSchistosoma mansoni invasion of the human host involves a variety of cross-species protein-protein interactions. The pathogen expresses a diverse arsenal of proteins that facilitate the breach of physical and biochemical barriers present in skin, evasion of the immune system, and digestion of human hemoglobin, allowing schistosomes to reside in the host for years. However, only a small number of specific interactions between S. mansoni and human proteins have been identified. We present and apply a protocol that generates testable predictions of S. mansoni-human protein interactions.\n\nMethodsIn this study, we first predict S. mansoni-human protein interactions based on similarity to known protein complexes. Putative interactions were then scored and assessed using several contextual filters, including the use of annotation automatically derived from literature using a simple natural language processing methodology. Our method predicted 7 out of the 10 previously known cross-species interactions.\n\nConclusionsSeveral predictions that warrant experimental follow-up were presented and discussed, including interactions involving potential vaccine candidate antigens, protease inhibition, and immune evasion. The application framework provides an integrated methodology for investigation of host-pathogen interactions and an extensive source of orthogonal data for experimental analysis. We have made the predictions available online for community perusal.\n\nAuthor SummaryThe S. mansoni parasite is the etiological agent of the disease Schistomiasis. However, protein-protein interactions have been experimentally characterized that relate to pathogenesis and establishment of infection. As with many pathogens, the understanding of these interactions is a key component for the development of new vaccines. In this project, we have applied a computational whole-genome comparative approach to aid in the prediction of interactions between S. mansoni and human proteins and to identify important proteins involved in infection. The results of applying this method recapitulate several previously characterized interactions, as well as suggest additional ones as potential therapeutic targets.

bioinformatics

Multi-scale Bayesian modeling of cryo-electron microscopy density maps

SummaryCryo-electron microscopy (cryo-EM) has become a mainstream technique for determining the structures of complex biological systems. However, accurate integrative structural modeling has been hampered by the challenges in objectively weighing cryo-EM data against other sources of information due to the presence of random and systematic errors, as well as correlations, in the data. To address these challenges, we introduce a Bayesian scoring function that efficiently and accurately ranks alternative structural models of a macromolecular system based on their consistency with a cryo-EM density map and other experimental and prior information. The accuracy of this approach is benchmarked using complexes of known structure and illustrated in three applications: the structural determination of the GroEL/GroES, RNA polymerase II, and exosome complexes. The approach is implemented in the open-source Integrative Modeling Platform (http://integrativemodeling.org), thus enabling integrative structure determination by combining cryo-EM data with other sources of information.\n\nHighlightsO_LIWe present a modeling approach to integrate cryo-EM data with other sources of information\nC_LIO_LIWe benchmark our approach using synthetic data on 21 complexes of known structure\nC_LIO_LIWe apply our approach to the GroEL/GroES, RNA polymerase II, and exosome complexes\nC_LI

bioinformatics