Classification of Complete Proteomes of Different Organisms and Protein Sets Based on Their Protein Distributions in Terms of Some Key Attributes of Proteins

AlphaFold2 models indicate that protein sequence determines both structure and dynamics

AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (> 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of [0, 1]. We found that for most protein models, including large proteins and protein complexes, the AF2-scores are highly correlated with the root mean square fluctuations (RMSF) calculated from MD simulations. However, for an IDP and a randomized protein, the AF2-scores do not correlate with the RMSF from MD, especially for the IDP. Our results indicate that the protein structures predicted by AF2 also convey information of the residue flexibility, i.e., protein dynamics.

The Importance of Protein Phosphorylation for Signaling and Metabolism in Response to Diel Light Cycling and Nutrient Availability in a Marine Diatom

Diatoms are major contributors to global primary production and their populations in the modern oceans are affected by availability of iron, nitrogen, phosphate, silica, and other trace metals, vitamins, and infochemicals. However, little is known about the role of phosphorylation in diatoms and its role in regulation and signaling. We report a total of 2759 phosphorylation sites on 1502 proteins detected in Phaeodactylum tricornutum. Conditionally phosphorylated peptides were detected at low iron (n = 108), during the diel cycle (n = 149), and due to nitrogen availability (n = 137). Through a multi-omic comparison of transcript, protein, phosphorylation, and protein homology, we identify numerous proteins and key cellular processes that are likely under control of phospho-regulation. We show that phosphorylation regulates: (1) carbon retrenchment and reallocation during growth under low iron, (2) carbon flux towards lipid biosynthesis after the lights turn on, (3) coordination of transcription and translation over the diel cycle and (4) in response to nitrogen depletion. We also uncover phosphorylation sites for proteins that play major roles in diatom Fe sensing and utilization, including flavodoxin and phytotransferrin (ISIP2A), as well as identify phospho-regulated stress proteins and kinases. These findings provide much needed insight into the roles of protein phosphorylation in diel cycling and nutrient sensing in diatoms.

A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon's Information Theory

We propose a framework to convert the protein intrinsic disorder content to structural entropy (H) using Shannon’s information theory (IT). The structural capacity (C), which is the sum of H and structural information (I), is equal to the amino acid sequence length of the protein. The structural entropy of the residues expands a continuous spectrum, ranging from 0 (fully ordered) to 1 (fully disordered), consistent with Shannon’s IT, which scores the fully-determined state 0 and the fully-uncertain state 1. The intrinsically disordered proteins (IDPs) in a living cell may participate in maintaining the high-energy-low-entropy state. In addition, under this framework, the biological functions performed by proteins and associated with the order or disorder of their 3D structures could be explained in terms of information-gains or entropy-losses, or the reverse processes.