Author: xiaogli

MHC-Fine: Enhancing AlphaFold for Precise MHC-Peptide Interaction Prediction

xiaogli

The precise prediction of major histocompatibility complex (MHC)-peptide complex structures is pivotal for understanding cellular immune responses and advancing vaccine design. In our latest study, published in Biophysical Journal, we have enhanced AlphaFold’s capabilities by fine-tuning it with a specialized dataset consisting exclusively of high-resolution class I MHC-peptide crystal structures.

AlphaFold, while broadly effective, lacked the granularity necessary for the high-precision demands of class I MHC-peptide interaction prediction. Our tailored approach addresses this by providing a more detailed and accurate model. A comparative analysis was conducted against the homology-modeling-based method Pandora, as well as the AlphaFold multimer model. Our fine-tuned model demonstrates superior performance, with a median root-mean-square deviation (RMSD) for Cα atoms in peptides of 0.66 Å and improved predicted local distance difference test scores.

Moreover, our additional comparisons with AlphaFold3 on new MHC-I structures from the Protein Data Bank (PDB) published after January 1, 2023, show that our model has 15% more samples under 1 Å deviation, highlighting its enhanced precision.

These advances have substantial implications for computational immunology, potentially accelerating the development of novel therapeutics and vaccines by providing a more precise computational lens through which to view MHC-peptide interactions.

ClusPro AbEMap Server: predicting antibody epitopes

xiaogli

We developed a novel approach for modeling antibodies in complex with their corresponding antigens, and incorporated it as an Advanced function of the ClusPro Server. The Antibody-Epitope Mapping (AbEMap) Server allows the user to predict antibody-antigen interactions with three types of inputs: (i) X-ray structures, (ii) computationally predicted structures, and (iii) simply amino acid sequences. The details of processing these three input types and differences in efficiencies are discussed in this publication in Nature Protocols.

High Accuracy Prediction of PROTAC complex structures

xiaogli

A novel method to aid in design of PROTACs was developed by our group and published in JACS!

PROTAC – PROteolysis TArgeting Chimera is a heterobifunctional drug-like molecule that hijacks the Ubiquitin-Proteasome System (UPS) in mammalian cells and catalytically drives the process of ubiquitination of our protein of interest. The ubiquitinated proteins then are recognized and degraded by the native proteasome system of the cell.

In this work, we present a computational modeling approach that drastically reduces the cost of novel PROTAC design, also considering that synthesizing PROTAC molecules is often a challenge. In our publication, we’ve shown that our method is successfully predicting the benchmark datasets based on calculated Weighted Sum Potentials, and is especially precise in deriving preferred linker lengths and linker attachment points.

A novel structural systems biology approach

xiaogli

In a collaboration with Boston University, we developed a new, faster approach in investigating the interactome using mass spectrometry and applied it to reveal and understand mechanisms that drive the malignant cell phenotype formation. This work resulted in two publications in Nature Communications.

In our first publication, we introduced a new multiplex Co-fractionation/Mass Spectrometry (mCF/MS) platform that is more technically efficient, cost-effective and faster than previously reported Co-fractionation/Mass Spectrometry (CF/MS) methods. The mCF/MS approach was applied to compare the global protein interactome of mammary epithelial cells to the Protein Interaction Network (PIN) of two breast cancer cell lines, where many multimolecular complexes that drive malignant cell formation were described and investigated.

In the second publication based on our work, we introduced PAMAF: a Parallelized multidimensional analytic framework that examines 12 modalities: protein abundance in whole-cells, nuclei, exosomes, secretomed and membranes; N-glycosylation, phosphorylation; metabolites; mRNA, miRNA; and, in parallel, single-cell transcriptomes. Using this method, we explored the key proteins in the process of Epithelial to Mesenchymal Transition.