An agnostic analysis of the human AlphaFold2 proteome using local protein conformations

Transposable element exonization generates a reservoir of evolving and functional protein isoforms

Alternative splicing enhances protein diversity in different ways, including through exonization of transposable elements (TEs). Recent transcriptomic analyses identified thousands of unannotated spliced transcripts with exonizing TEs, but their contribution to the proteome and biological relevance remains unclear. Here, we use transcriptome assembly, ribosome profiling, and proteomics to describe a population of 1,227 unannotated TE exonizing isoforms generated by mRNA splicing and recurrent in human populations. Despite being shorter and lowly expressed, these isoforms are shared between individuals and efficiently translated. Functional analyses show stable expression, specific cellular localization, and, in some cases, modified functions. Exonized TEs are rich in ancient genes, whereas the involved splice sites are recent and can be evolutionarily conserved. In addition, exonized TEs contribute to the secondary structure of the emerging isoforms, supporting their functional relevance. We conclude that TE-spliced isoforms represent a diversity reservoir of functional proteins on which natural selection can act.

Special Issue: "Molecular Dynamics Simulations and Structural Analysis of Protein Domains"

The 3D protein structure is the basis for all their biological functions [...].

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

A poly-proline II helix in YadA from Yersinia enterocolitica serotype O:9 facilitates heparin binding through electrostatic interactions

Poly-proline II helices are secondary structure motifs frequently found in ligand-binding sites. They exhibit increased flexibility and solvent exposure compared to the strongly hydrogen-bonded α-helices or β-strands and can therefore easily be misinterpreted as completely unstructured regions with an extremely high rotational freedom. Here, we show that the adhesin YadA of Yersinia enterocolitica serotype O:9 contains a poly-proline II helix interaction motif in the N-terminal region. The motif is involved in the interaction of YadAO:9 with heparin, a host glycosaminoglycan. We show that the basic residues within the N-terminal motif of YadA are required for electrostatic interactions with the sulfate groups of heparin. Biophysical methods including CD spectroscopy, solution-state NMR and SAXS all independently support the presence of a poly-proline helix allowing YadAO:9 binding to the rigid heparin. Lastly, we show that host cells deficient in sulfation of heparin and heparan sulfate are not targeted by YadAO:9 -mediated adhesion. We speculate that the YadAO:9 -heparin interaction plays an important and highly strain-specific role in the pathogenicity of Yersinia enterocolitica serotype O:9.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.