AlphaFold2 and RoseTTAFold predict posttranslational modifications. Chromophore formation in GFP-like proteins

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.

Next generation of multispecific antibody engineering

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

Surface exposed and charged residues drive thermostability in fungi

Fungi, though mesophilic, include thermophilic and thermostable species, as well. The thermostability of proteins observed in these fungi is most likely to be attributed to several molecular factors, such as the presence of salt bridges and hydrogen bond interactions between side chains. These factors cannot be generalized for all fungi. Factors impacting thermostability can guide how fungal thermophilic proteins gain thermostability. We curated a dataset of proteins for 14 thermophilic fungi and their evolutionarily closer mesophiles. Additionally, the proteome of Chaetomium thermophilum and its evolutionarily related mesophile Chaetomium globosum was analyzed. Using eggNOG, we categorized the proteomes into clusters of orthologous groups (COGs). While the individual count of proteins is over-represented in mesophiles (for COGs S, G, L, and Q), there are certain features that are significantly enriched in thermophiles (such as charged residues, exposed residues, polar residues, etc.). Since fungi are known to be cellulolytic and chitinolytic by nature, we selected 37 existing carbohydrate-active enzymes (CAZyme) families in Eurotiales, Mucorales, and Sordariales. We looked at closely similar sequences and their modeled structures for further comparison. Comparing solvent accessibilities of thermophilic and mesophilic proteins, exposed and intermediate residues are observed higher in thermophiles whereas buried residues are observed higher in mesophiles. For specific five CAZYme families (GH7, GH11, GH18, GH45, and CBM1) we looked at position-specific substitutions between thermophiles and mesophiles. We also found that there are relatively more intramolecular interactions in thermophiles compared to mesophiles. Thus, we found factors such as surface exposed residues and charged residues that are highly likely to impart thermostability in fungi, and this study sets the stage for further studies in the area of fungal thermostability.

Multicolor lifetime imaging and its application to HIV-1 uptake

Simultaneous imaging of nine fluorescent proteins is demonstrated in a single acquisition using fluorescence lifetime imaging microscopy combined with pulsed interleaved excitation of three laser lines. Multicolor imaging employing genetically encodable fluorescent proteins permits spatio-temporal live cell imaging of multiple cues. Here, we show that multicolor lifetime imaging allows visualization of quadruple labelled human immunodeficiency viruses on host cells that in turn are also labelled with genetically encodable fluorescent proteins. This strategy permits to simultaneously visualize different sub-cellular organelles (mitochondria, cytoskeleton, and nucleus) during the process of virus entry with the potential of imaging up to nine different spectral channels in living cells.