Modularity in protein structures: study on all-alpha proteins

Computational analysis of the sequence-structure relation in SARS-CoV-2 spike protein using protein contact networks

The structure of proteins impacts directly on the function they perform. Mutations in the primary sequence can provoke structural changes with consequent modification of functional properties. SARS-CoV-2 proteins have been extensively studied during the pandemic. This wide dataset, related to sequence and structure, has enabled joint sequence-structure analysis. In this work, we focus on the SARS-CoV-2 S (Spike) protein and the relations between sequence mutations and structure variations, in order to shed light on the structural changes stemming from the position of mutated amino acid residues in three different SARS-CoV-2 strains. We propose the use of protein contact network (PCN) formalism to: (i) obtain a global metric space and compare various molecular entities, (ii) give a structural explanation of the observed phenotype, and (iii) provide context dependent descriptors of single mutations. PCNs have been used to compare sequence and structure of the Alpha, Delta, and Omicron SARS-CoV-2 variants, and we found that omicron has a unique mutational pattern leading to different structural consequences from mutations of other strains. The non-random distribution of changes in network centrality along the chain has allowed to shed light on the structural (and functional) consequences of mutations.

New simulation insights on the structural transition mechanism of bovine rhodopsin activation

Inactive rhodopsin can absorb photons, which induces different structural transitions that finally activate rhodopsin. We have examined the change in spatial configurations and physicochemical factors that result during the transition mechanism from the inactive to the active rhodopsin state via intermediates. During the activation process, many existing atomic contacts are disrupted, and new ones are formed. This is related to the movement of Helix 5, which tilts away from Helix 3 in the intermediate state in lumirhodopsin and moves closer to Helix 3 again in the active state. Similar patterns of changing atomic contacts are observed between Helices 3 and 5 of the adenosine and neurotensin receptors. In addition, residues 220-238 of rhodopsin, which are disordered in the inactive state, fold in the active state before binding to the Gα, where it catalyzes GDP/GTP exchange on the Gα subunit. Finally, molecular dynamics simulations in the membrane environment revealed that the arrestin binding region adopts a more flexible extended conformation upon phosphorylation, likely promoting arrestin binding and inactivation. In summary, our results provide additional structural understanding of specific rhodopsin activation which might be relevant to other Class A G protein-coupled receptor proteins.

Structural analysis of SARS-CoV-2 Spike protein variants through graph embedding

Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected almost all countries. The unprecedented spreading of this virus has led to the insurgence of many variants that impact protein sequence and structure that need continuous monitoring and analysis of the sequences to understand the genetic evolution and to prevent possible dangerous outcomes. Some variants causing the modification of the structure of the proteins, such as the Spike protein S, need to be monitored. Protein contact networks (PCNs) have been recently proposed as a modelling framework for protein structures. In such a framework, the protein structure is represented as an unweighted graph whose nodes are the central atoms of the backbones (C- α ), and edges connect two atoms falling in the spatial distance between 4 and 7 Å. PCN may also be a data-rich representation since we may add to each node/atom biological and topological information. Such formalism enables the possibility of using algorithms from graph theory to analyze the graph. In particular, we refer to graph embedding methods enabling the analysis of such graphs with deep learning methods. In this work, we explore the possibility of embedding PCN using Graph Neural Networks and then analyze in the embedded space each residue to distinguish mutated residues from non-mutated ones. In particular, we analyzed the structure of the Spike protein of the coronavirus. First, we obtained the PCNs of the Spike protein for the wild-type, α , β , and δ variants. Then we used the GraphSage embedding algorithm to obtain an unsupervised embedding. Then we analyzed the point of mutation in the embedded space. Results show the characteristics of the mutation point in the embedding space.

PCN-Miner: an open-source extensible tool for the analysis of Protein Contact Networks

MOTIVATION

Protein Contact Network (PCN) is a powerful method for analysing the structure and function of proteins, with a specific focus on disclosing the molecular features of allosteric regulation through the discovery of modular substructures. The importance of PCN analysis has been shown in many contexts, such as the analysis of SARS-CoV-2 Spike protein and its complexes with the Angiotensin Converting Enzyme 2 (ACE2) human receptors. Even if there exist many software tools implementing such methods, there is a growing need for the introduction of tools integrating existing approaches.

RESULTS

We present PCN-Miner, a software tool implemented in the Python programming language, able to (i) import protein structures from the Protein Data Bank; (ii) generate the corresponding PCN; (iii) model, analyse and visualize PCNs and related protein structures by using a set of known algorithms and metrics. The PCN-Miner can cover a large set of applications: from clustering to embedding and subsequent analysis.

AVAILABILITY AND IMPLEMENTATION

The PCN-Miner tool is freely available at the following GitHub repository: https://github.com/hguzzi/ProteinContactNetworks. It is also available in the Python Package Index (PyPI) repository.

Pseudo-Symmetric Assembly of Protodomains as a Common Denominator in the Evolution of Polytopic Helical Membrane Proteins

The polytopic helical membrane proteome is dominated by proteins containing seven transmembrane helices (7TMHs). They cannot be grouped under a monolithic fold or superfold. However, a parallel structural analysis of folds around that magic number of seven in distinct protein superfamilies (SWEET, PnuC, TRIC, FocA, Aquaporin, GPCRs) reveals a common homology, not in their structural fold, but in their systematic pseudo-symmetric construction during their evolution. Our analysis leads to guiding principles of intragenic duplication and pseudo-symmetric assembly of ancestral transmembrane helical protodomains, consisting of 3 (or 4) helices. A parallel deconstruction and reconstruction of these domains provides a structural and mechanistic framework for their evolutionary paths. It highlights the conformational plasticity inherent to fold formation itself, the role of structural as well as functional constraints in shaping that fold, and the usefulness of protodomains as a tool to probe convergent vs divergent evolution. In the case of FocA vs. Aquaporin, this protodomain analysis sheds new light on their potential divergent evolution at the protodomain level followed by duplication and parallel evolution of the two folds. GPCR domains, whose function does not seem to require symmetry, nevertheless exhibit structural pseudo-symmetry. Their construction follows the same protodomain assembly as any other pseudo-symmetric protein suggesting their potential evolutionary origins. Interestingly, all the 6/7/8TMH pseudo-symmetric folds in this study also assemble as oligomeric forms in the membrane, emphasizing the role of symmetry in evolution, revealing self-assembly and co-evolution not only at the protodomain level but also at the domain level.

Structural modules of the stress-induced protein HflX: an outlook on its evolution and biological role

Multifunctional proteins often show modular structures. A functional domain and the structural modules within the domain show evolutionary conservation of their spatial arrangement since that gives the protein its functionality. However, the question remains as to how members of different domains of life (Archaea, Bacteria, Eukarya), polish and perfect these modules within conserved multidomain proteins, to tailor functional proteins according to their specific requirements. In the quest for plausible answers to this question, we studied the bacterial protein HflX. HflX is a universally conserved member of the Obg-GTPase superfamily but its functional role in Archaea and Eukarya is barely known. It is a multidomain protein and possesses, in addition to its conserved GTPase domain, an ATP-binding N-terminal domain. It is involved in heat stress response in Escherichia coli and our laboratory recently identified an ATP-dependent RNA helicase activity of E. coli HflX, which is likely instrumental in rescuing ribosomes during heat stress. Because perception and response to stress is expected to be different in different life forms, the question is whether this activity is preserved in higher organisms or not. Thus, we explored the evolution pattern of different structural modules of HflX, with particular emphasis on the ATP-binding domain, to understand plausible biological role of HflX in other forms of life. Our analyses indicate that, while the evolutionary pattern of the GTPase domain follows a conserved phylogeny, conservation of the ATP-binding domain shows a complicated pattern. The limited analysis described here hints towards possible evolutionary adaptations and modifications of the domain, something which needs to be investigated in more depth in homologs from other life forms. Deciphering how nature 'tweaks' such modules, both structurally and functionally, may help in understanding the evolution of such proteins, and, on a large-scale, of stress-related proteins in general as well.

ProLego: tool for extracting and visualizing topological modules in protein structures

Background

In protein design, correct use of topology is among the initial and most critical feature. Meticulous selection of backbone topology aids in drastically reducing the structure search space. With ProLego, we present a server application to explore the component aspect of protein structures and provide an intuitive and efficient way to scan the protein topology space.

Result

We have implemented in-house developed “topological representation” in an automated-pipeline to extract protein topology from given protein structure. Using the topology string, ProLego, compares topology against a non-redundant extensive topology database (ProLegoDB) as well as extracts constituent topological modules. The platform offers interactive topology visualization graphs.

Conclusion

ProLego, provides an alternative but comprehensive way to scan and visualize protein topology along with an extensive database of protein topology. ProLego can be found at http://www.proteinlego.com

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2171-9) contains supplementary material, which is available to authorized users.