Exploring the potential of a structural alphabet-based tool for mining multiple target conformations and target flexibility insight

Epithelial-mesenchymal transition-related gene signature for prognosis of lung squamous cell carcinoma

Epithelial-mesenchymal transition (EMT) is associated with tumor invasion and progression, and is regulated by DNA methylation. A prognostic signature of lung squamous cell carcinoma (LUSC) with EMT-related gene data has not yet been established. In our study, we constructed a co-expression network using differentially expressed genes (DEGs) obtained from The Cancer Genome Atlas (TCGA) to identify hub genes. We conducted a correlation analysis between the differentially methylated hub genes and differentially expressed EMT-related genes to screen EMT-related differentially methylated genes (ERDMGs). Functional enrichment was performed to annotate the ERDMGs. The least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses were performed to build a survival prognosis prediction model. Additionally, druggability analysis was performed to predict the potential drug targets of ERDMGs. We screened 11 ERDMGs that were enriched in cell adhesion molecules and other signaling pathways. Finally, we constructed a 4-ERDMG model, which showed good ability to predict survival prognosis in the training and validation sets. The model could serve as an independent predictive factor for patients with LUSC. Additionally, our druggability analysis predicted that CC chemokine ligand 23 (CCL23) and Hepatocyte nuclear factor 1b (HNF1B) may be the underlying drug targets of LUSC. We established a new risk score (RS) system as a prognostic indicator to predict the outcome of patients with LUSC, which will help in the improvement of treatment strategies.

Disentangling the complexity of low complexity proteins

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs.

Short abstract

There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

Impacts of drug resistance mutations on the structural asymmetry of the HIV-2 protease

Background

Drug resistance is a severe problem in HIV treatment. HIV protease is a common target for the design of new drugs for treating HIV infection. Previous studies have shown that the crystallographic structures of the HIV-2 protease (PR2) in bound and unbound forms exhibit structural asymmetry that is important for ligand recognition and binding. Here, we investigated the effects of resistance mutations on the structural asymmetry of PR2. Due to the lack of structural data on PR2 mutants, the 3D structures of 30 PR2 mutants of interest have been modeled using an in silico protocol. Structural asymmetry analysis was carried out with an in-house structural-alphabet-based approach.

Results

The systematic comparison of the asymmetry of the wild-type structure and a large number of mutants highlighted crucial residues for PR2 structure and function. In addition, our results revealed structural changes induced by PR2 flexibility or resistance mutations. The analysis of the highlighted structural changes showed that some mutations alter protein stability or inhibitor binding.

Conclusions

This work consists of a structural analysis of the impact of a large number of PR2 resistant mutants based on modeled structures. It suggests three possible resistance mechanisms of PR2, in which structural changes induced by resistance mutations lead to modifications in the dimerization interface, ligand recognition or inhibitor binding.

Exploration of the effects of sequence variations between HIV-1 and HIV-2 proteases on their three-dimensional structures

HIV protease inhibitors (PIs) approved by the FDA (US Food and Drug Administration) are a major class of antiretroviral. HIV-2 protease (PR2) is naturally resistant to most of them as PIs were designed for HIV-1 protease (PR1). In this study, we explored the impact of amino-acid substitutions between PR1 and PR2 on the structure of protease (PR) by comparing the structural variability of 13 regions using 24 PR1 and PR2 structures complexed with diverse ligands. Our analyses confirmed structural rigidity of the catalytic region and highlighted the important role of three regions in the conservation of the catalytic region conformation. Surprisingly, we showed that the flap region, corresponding to a flexible region, exhibits similar conformations in PR1 and PR2. Furthermore, we identified regions exhibiting different conformations in PR1 and PR2, which could be explained by the intrinsic flexibility of these regions, by crystal packing, or by PR1 and PR2 substitutions. Some substitutions induce structural changes in the R2 and R4 regions that could have an impact on the properties of PI-binding site and could thus modify PI binding mode. Substitutions involved in structural changes in the elbow region could alter the flexibility of the PR2 flap regions relative to PR1, and thus play a role in the transition from the semi-open form to the closed form, and have an impact on ligand binding. These results improve the understanding of the impact of sequence variations between PR1 and PR2 on the natural resistance of HIV-2 to commercially available PIs.Communicated by Ramaswamy H. Sarma.

Repeatability in protein sequences

Low complexity regions (LCRs) in protein sequences have special properties that are very different from those of globular proteins. The rules that define secondary structure elements do not apply when the distribution of amino acids becomes biased. While there is a tendency towards structural disorder in LCRs, various examples, and particularly homorepeats of single amino acids, suggest that very short repeats could adopt structures very difficult to predict. These structures are possibly variable and dependant on the context of intra- or inter-molecular interactions. In general, short repeats in LCRs can induce structure. This could explain the observation that very short (non-perfect) repeats are widespread and many define regions with a function in protein interactions. For these reasons, we have developed an algorithm to quickly analyze local repeatability along protein sequences, that is, how close a protein fragment is from a perfect repeat. Using this algorithm we identified that the proteins of the yeast Saccharomyces cerevisiae are depleted in short repeats (approximate or not) of odd-length, while the human proteins are not, that the fish Danio rerio has many proteins with repeats of length two and that the plant Arabidopsis thaliana has an unusually large amount of repeats of length seven. Our method (REpeatability Scanner, RES, accessible at http://cbdm-01.zdv.uni-mainz.de/~munoz/res/) allows to find regions with approximate short repeats in protein sequences, and helps to characterize the variable use of LCRs and compositional bias in different organisms.

Characterizing the structural variability of HIV-2 protease upon the binding of diverse ligands using a structural alphabet approach

The HIV-2 protease (PR2) is an important target for designing new drugs against the HIV-2 infection. In this study, we explored the structural backbone variability of all available PR2 structures complexed with various inhibitors using a structural alphabet approach. 77% of PR2 positions are structurally variable, meaning they exhibit different local conformations in PR2 structures. This variability was observed all along the structure, particularly in the elbow and flap regions. A part of these backbone changes observed between the 18 PR2 is induced by intrinsic flexibility, and ligand binding putatively induces others occurring in the binding pocket. These latter changes could be important for PR2 adaptation to diverse ligands and are accompanied by changes outside the binding pocket. In addition, the study of the link between structural variability of the pocket and PR2-ligand interactions allowed us to localize pocket regions important for ligand binding and catalytic function, regions important for ligand recognition that adjust their backbone in response to ligand binding and regions important for the pocket opening and closing that have large intrinsic flexibility. Finally, we suggested that differences in ligand effectiveness for PR2 could be partially explained by different backbone deformations induced by these ligands. To conclude, this study is the first characterization of the PR2 structural variability considering ligand diversity. It provides information about the recognition of PR2 to various ligands and its mechanisms to adapt its local conformation to bound ligands that could help understand the resistance of PR2 to its inhibitors, a major antiretroviral class. Communicated by Ramaswamy H. Sarma.

Characterization of HIV-2 Protease Structure by Studying Its Asymmetry at the Different Levels of Protein Description

HIV-2 protease (PR2) is a homodimer, which is an important target in the treatment of the HIV-2 infection. In this study, we developed an in silico protocol to analyze and characterize the asymmetry of the unbound PR2 structure using three levels of protein description by comparing the conformation, accessibility, and flexibility of each residue in the two PR2 chains. Our results showed that 65% of PR2 residues have at least one of the three studied asymmetries (structural, accessibility, or flexibility) with 10 positions presenting the three asymmetries in the same time. In addition, we noted that structural and flexibility asymmetries are linked indicating that the structural asymmetry of some positions result from their large flexibility. By comparing the structural asymmetry of the crystallographic and energetically minimized structures of the unbound PR2, we confirmed that the structural asymmetry of unbound PR2 is an intrinsic property of this protein with an important role for the PR2 deformation upon ligand binding. This analysis also allowed locating asymmetries corresponding to crystallization artefacts. This study provides insight that will help to better understand the structural deformations of PR2 and to identify key positions for ligand binding.

SAFlex: A structural alphabet extension to integrate protein structural flexibility and missing data information

In this paper, we describe SAFlex (Structural Alphabet Flexibility), an extension of an existing structural alphabet (HMM-SA), to better explore increasing protein three dimensional structure information by encoding conformations of proteins in case of missing residues or uncertainties. An SA aims to reduce three dimensional conformations of proteins as well as their analysis and comparison complexity by simplifying any conformation in a series of structural letters. Our methodology presents several novelties. Firstly, it can account for the encoding uncertainty by providing a wide range of encoding options: the maximum a posteriori, the marginal posterior distribution, and the effective number of letters at each given position. Secondly, our new algorithm deals with the missing data in the protein structure files (concerning more than 75% of the proteins from the Protein Data Bank) in a rigorous probabilistic framework. Thirdly, SAFlex is able to encode and to build a consensus encoding from different replicates of a single protein such as several homomer chains. This allows localizing structural differences between different chains and detecting structural variability, which is essential for protein flexibility identification. These improvements are illustrated on different proteins, such as the crystal structure of an eukaryotic small heat shock protein. They are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility.