Conformational dynamics of α-1 acid glycoprotein (AGP) in cancer: A comparative study of glycosylated and unglycosylated AGP

α-1 acid glycoprotein (AGP) is one of the most abundant plasma proteins. It fulfills two important functions: immunomodulation, and binding to various drugs and receptors. These different functions are closely associated and modulated via changes in glycosylation and cancer missense mutations. From a structural point of view, glycans alter the local biophysical properties of the protein leading to a diverse ligand-binding spectrum. However, glycans can typically not be observed in the resolved X-ray crystallography structure of AGP due to their high flexibility and microheterogeneity, so limiting our understanding of AGP's conformational dynamics 70 years after its discovery. We here investigate how mutations and glycosylation interfere with AGP's conformational dynamics changing its biophysical behavior, by using molecular dynamics (MD) simulations and sequence-based dynamics predictions. The MD trajectories show that glycosylation decreases the local backbone flexibility of AGP and increases the flexibility of distant regions through allosteric effects. We observe that mutations near the glycosylation site affect glycan's conformational preferences. Thus, we conclude that mutations control glycan dynamics which modulates the protein's backbone flexibility directly affecting its accessibility. These findings may assist in the drug design targeting AGP's glycosylation and mutations in cancer.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

b2bTools: online predictions for protein biophysical features and their conservation

We provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of our predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or text input of a sequence provides integrated predictions from DynaMine backbone and side-chain dynamics, conformational propensities, and derived EFoldMine early folding, DisoMine disorder, and Agmata β-sheet aggregation. These predictions, several of which were previously not available online, capture 'emergent' properties of proteins, i.e. the inherent biophysical propensities encoded in their sequence, rather than context-dependent behaviour (e.g. final folded state). In addition, upload of a multiple sequence alignment (MSA) in a variety of formats enables exploration of the biophysical variation observed in homologous proteins. The associated plots indicate the biophysical limits of functionally relevant protein behaviour, with unusual residues flagged by a Gaussian mixture model analysis. The prediction results are available as JSON or CSV files and directly accessible via an API. Online visualisation is available as interactive plots, with brief explanations and tutorial pages included. The server and API employ an email-free token-based system that can be used to anonymously access previously generated results.

Screening of native plants and algae growing on fly-ash affected areas near National Thermal Power Corporation, Tanda, Uttar Pradesh, India for accumulation of toxic heavy metals

The present investigation was carried out to screen native plants growing in fly-ash (FA) contaminated areas near National Thermal Power Corporation, Tanda, Uttar Pradesh, India with a view to using them for the eco-restoration of the area. A total number of 17 plants (9 aquatic, 6 terrestrial and 2 algal species) were collected and screened for heavy metal (Fe, Zn, Cu, Mo, B, Si, Al, Cr, Pb, Cd, Hg and As) accumulation. Differential accumulation of various heavy metals by different species of plants was observed. Hydrilla verticillata was found to be the most efficient metal accumulator among 9 aquatic plants, Eclipta alba among 6 terrestrial plants and Phormedium papyraceum between 2 algal species. In general, the maximum levels of most metals were found in terrestrial plants while the lowest in algal species. However, translocation of the metals from root to shoot was found to be higher in aquatic plants than terrestrial ones. These results suggest that various aquatic, terrestrial and algal species of plants may be used in a synergistic way to remediate and restore the FA contaminated areas.

Identification of a renal-specific oxido-reductase in newborn diabetic mice

Aldose reductase (ALR2), a NADPH-dependent aldo-keto reductase (AKR), is widely distributed in mammalian tissues and has been implicated in complications of diabetes, including diabetic nephropathy. To identify a renal-specific reductase belonging to the AKR family, representational difference analyses of cDNA from diabetic mouse kidney were performed. A full-length cDNA with an ORF of 855 nt and yielding a approximately 1.5-kb mRNA transcript was isolated from a mouse kidney library. Human and rat homologues also were isolated, and they had approximately 91% and approximately 97% amino acid identity with mouse protein. In vitro translation of the cDNA yielded a protein product of approximately 33 kDa. Northern and Western blot analyses, using the cDNA and antirecombinant protein antibody, revealed its expression exclusively confined to the kidney. Like ALR2, the expression was up-regulated in diabetic kidneys. Its mRNA and protein expression was restricted to renal proximal tubules. The gene neither codistributed with Tamm-Horsfall protein nor aquaporin-2. The deduced protein sequence revealed an AKR-3 motif located near the N terminus, unlike the other AKR family members where it is confined to the C terminus. Fluorescence quenching and reactive blue agarose chromatography studies revealed that it binds to NADPH with high affinity (K(dNADPH) = 66.9 +/- 2.3 nM). This binding domain is a tetrapeptide (Met-Ala-Lys-Ser) located within the AKR-3 motif that is similar to the other AKR members. The identified protein is designated as RSOR because it is renal-specific with properties of an oxido-reductase, and like ALR2 it may be relevant in the renal complications of diabetes mellitus.