Alireza Omidi

Interactions mediated by intrinsically disordered protein regions (IDRs) pose formidable challenges in structural characterization. IDRs are highly versatile, capable of adopting diverse structures and engagement modes. Motivated by recent strides in protein structure prediction, we embarked on exploring the extent to which AlphaFold-Multimer can faithfully reproduce the intricacies of interactions involving IDRs. To this end, we gathered multiple datasets covering the versatile spectrum of IDR binding modes and used them to probe AlphaFold-Multimer's prediction of IDR interactions and their dynamics. Our analyses revealed that AlphaFold-Multimer is not only capable of predicting various types of bound IDR structures with high success rate, but that distinguishing true interactions from decoys, and unreliable predictions from accurate ones is achievable by appropriate use of AlphaFold-Multimer's intrinsic scores. We found that the quality of predictions drops for more heterogeneous, fuzzy interaction types, most likely due to lower interface hydrophobicity and higher coil content. Notably though, certain AlphaFold-Multimer scores, such as the Predicted Aligned Error and residue-ipTM, are highly correlated with structural heterogeneity of the bound IDR, enabling clear distinctions between predictions of fuzzy and more homogeneous binding modes. Finally, our benchmarking revealed that predictions of IDR interactions can also be successful when using full-length proteins, but not as accurate as with cognate IDRs. To facilitate identification of the cognate IDR of a given partner, we established "minD," which pinpoints potential interaction sites in a full-length protein. Our study demonstrates that AlphaFold-Multimer can correctly identify interacting IDRs and predict their mode of engagement with a given partner.

Abstract Constructing joint representations from multimodal single-cell datasets is crucial for understanding cellular heterogeneity and function. Traditional methods, such as factor analysis and kNN-based approaches, face computational limitations with scalability across large datasets and multiple modalities. In this work, we demonstrate the product-of-experts VAE-based model, which offers a flexible, scalable solution for integrating multimodal data, allowing for the seamless mapping of both unimodal and multimodal queries onto a reference atlas. We evaluate how different strategies for combining modalities in the VAE framework impact query-to-reference mapping across diverse datasets, including CITE-seq and spatial metabolomics. Our benchmarks assess batch effect correction, biological signal preservation, and imputation of missing modalities. We showcase our approach in a mosaic setting, integrating CITE-seq and multiome data to accurately map unimodal and multimodal queries into the joint latent space. We extend this to spatial data by integrating gene expression and metabolomics from paired Visium and MALDI-MSI slides, achieving a high correlation in metabolite predictions from spatial gene expression. Our results demonstrate that this VAE-based framework is scalable, robust, and easily applicable across multiple modalities, providing a powerful tool for data imputation, querying, and biological discovery.

Many proteins function by toggling between distinct conformations, yet most structure predictors have been trained on data that do not capture this conformational diversity. Here, we benchmarked AlphaFold2, AlphaFold3, and recent variants on autoinhibited proteins, a class of allosterically regulated, often multi-domain proteins that exist in equilibrium between active and autoinhibited states. Our analyses show that AlphaFold2 fails to reproduce the experimental structures of many autoinhibited proteins, which is reflected in reduced confidence scores. This contrasts sharply with its high-accuracy, high-confidence predictions of non-autoinhibited multi-domain proteins. When tested for its ability to capture conformational diversity, we found that AlphaFold2 performs better when combined with uniform subsampling of sequence alignments rather than local subsampling. BioEmu and AlphaFold3 improve upon these results, yet still struggle to accurately reproduce details of experimental structures. Together, our study underscores the persistent challenges of predicting protein structures shaped by complex energy landscapes. Although many proteins function by toggling between distinct conformations, most structure predictors remain limited to a single static fold. Here, the authors test the performance of AlphaFold2, AlphaFold3, and recent variants on a dataset of autoinhibited proteins exhibiting at least two functionally distinct conformations, and show that AlphaFold2 fails to reproduce the experimental structures of many autoinhibited proteins, but that it can capture conformational diversity when using uniform multiple sequence alignment subsampling.