Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Machine learning-based prediction of proteins' architecture using sequences of amino acids and structural alphabets

In addition to the growth of protein structures generated through wet laboratory experiments and deposited in the PDB repository, AlphaFold predictions have significantly contributed to the creation of a much larger database of protein structures. Annotating such a vast number of structures has become an increasingly challenging task. CATH is widely recognized as one the most common platforms for addressing this challenge, as it classifies proteins based on their structural and evolutionary relationships, offering the scientific community an invaluable resource for uncovering various properties, including functional annotations. While CATH annotation involves - to some extent - human intervention, keeping up with the classification of the rapidly expanding repositories of protein structures has become exceedingly difficult. Therefore, there is a pressing need for a fully automated approach. On the other hand, the abundance of protein sequences stemming from next generation sequencing technologies, lacking structural annotations, presents an additional challenge to the scientific community. Consequently, 'pre-annotating' protein sequences with structural features, ensuring a high level of precision, could prove highly advantageous. In this paper, after a thorough investigation, we introduce a novel machine-learning model capable of classifying any protein domain, whether it has a known structure or not, into one of the 40 main CATH Architectures. We achieve an F1 Score of 0.92 using only the amino acid sequence and a score of 0.94 using both the sequence of amino acids and the sequence of structural alphabets.Communicated by Ramaswamy H. Sarma.

Structural and Dynamic Differences between Calreticulin Mutants Associated with Essential Thrombocythemia

Essential thrombocythemia (ET) is a blood cancer. ET is characterized by an overproduction of platelets that can lead to thrombosis formation. Platelet overproduction occurs in megakaryocytes through a signaling pathway that could involve JAK2, MPL, or CALR proteins. CALR mutations are associated with 25–30% of ET patients; CALR variants must be dimerized to induce ET. We classified these variants into five classes named A to E; classes A and B are the most frequent classes in patients with ET. The dynamic properties of these five classes using structural models of CALR’s C-domain were analyzed using molecular dynamics simulations. Classes A, B, and C are associated with frameshifts in the C-domain. Their dimers can be stable only if a disulfide bond is formed; otherwise, the two monomers repulse each other. Classes D and E cannot be stable as dimers due to the absence of disulfide bonds. Class E and wild-type CALR have similar dynamic properties. These results suggest that the disulfide bond newly formed in classes A, B, and C may be essential for the pathogenicity of these variants. They also underline that class E cannot be directly related to ET but corresponds to human polymorphisms.

Analysis of Integrin αIIb Subunit Dynamics Reveals Long-Range Effects of Missense Mutations on Calf Domains

Integrin α_IIbβ₃, a glycoprotein complex expressed at the platelet surface, is involved in platelet aggregation and contributes to primary haemostasis. Several integrin α_IIbβ₃ polymorphisms prevent the aggregation that causes haemorrhagic syndromes, such as Glanzmann thrombasthenia (GT). Access to 3D structure allows understanding the structural effects of polymorphisms related to GT. In a previous analysis using Molecular Dynamics (MD) simulations of α_IIbCalf-1 domain structure, it was observed that GT associated with single amino acid variation affects distant loops, but not the mutated position. In this study, experiments are extended to Calf-1, Thigh, and Calf-2 domains. Two loops in Calf-2 are unstructured and therefore are modelled expertly using biophysical restraints. Surprisingly, MD revealed the presence of rigid zones in these loops. Detailed analysis with structural alphabet, the Proteins Blocks (PBs), allowed observing local changes in highly flexible regions. The variant P741R located at C-terminal of Calf-1 revealed that the Calf-2 presence did not affect the results obtained with isolated Calf-1 domain. Simulations for Calf-1 + Calf-2, and Thigh + Calf-1 variant systems are designed to comprehend the impact of five single amino acid variations in these domains. Distant conformational changes are observed, thus highlighting the potential role of allostery in the structural basis of GT.

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only three CDR loops from the single variable domain (V_HH) at the N-terminus of each heavy chain. This feature of the V_HH, along with their other important features, e.g., easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better V_HH domains, it is important to thoroughly understand their sequence and structure characteristics and relationship. In this study, sequence characteristics of V_HH domains have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of V_HH domains showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of V_HH, an exciting view of various factors in structure prediction of V_HH, like template framework selection, is also discussed.

PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks

This paper describes the development and application of a suite of tools, called PBxplore, to analyze the dynamics and deformability of protein structures using Protein Blocks (PBs). Proteins are highly dynamic macromolecules, and a classical way to analyze their inherent flexibility is to perform molecular dynamics simulations. The advantage of using small structural prototypes such as PBs is to give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, PBs allow analysis of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is able to process large amounts of data such as those produced by molecular dynamics simulations. It produces frequencies, entropy and information logo outputs as text and graphics. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the open-source MIT license.

In silico analysis of Glanzmann variants of Calf-1 domain of αIIbβ3 integrin revealed dynamic allosteric effect

Integrin α_IIbβ₃ mediates platelet aggregation and thrombus formation. In a rare hereditary bleeding disorder, Glanzmann thrombasthenia (GT), α_IIbβ₃ expression / function are impaired. The impact of deleterious missense mutations on the complex structure remains unclear. Long independent molecular dynamics (MD) simulations were performed for 7 GT variants and reference structure of the Calf-1 domain of α_IIb. Simulations were analysed using a structural alphabet to describe local protein conformations. Common and flexible regions as well as deformable zones were observed in all the structures. The most flexible region of Calf-1 (with highest B-factor) is rather a rigid region encompassed into two deformable zones. Each mutated structure barely showed any modifications at the mutation sites while distant conformational changes were observed. These unexpected results question the relationship between molecular dynamics and allostery; and the role of these long-range effects in the impaired α_IIbβ₃ expression. This method is aimed at studying all α_IIbβ₃ sub-domains and impact of missense mutations at local and global structural level.

Calculating and scoring high quality multiple flexible protein structure alignments

MOTIVATION

Calculating multiple protein structure alignments (MSAs) is important for understanding functional and evolutionary relationships between protein families, and for modeling protein structures by homology. While incorporating backbone flexibility promises to circumvent many of the limitations of rigid MSA algorithms, very few flexible MSA algorithms exist today. This article describes several novel improvements to the Kpax algorithm which allow high quality flexible MSAs to be calculated. This article also introduces a new Gaussian-based MSA quality measure called 'M-score', which circumvents the pitfalls of RMSD-based quality measures.

RESULTS

As well as calculating flexible MSAs, the new version of Kpax can also score MSAs from other aligners and from previously aligned reference datasets. Results are presented for a large-scale evaluation of the Homstrad, SABmark and SISY benchmark sets using Kpax and Matt as examples of state-of-the-art flexible aligners and 3DCOMB as an example of a state-of-the-art rigid aligner. These results demonstrate the utility of the M-score as a measure of MSA quality and show that high quality MSAs may be achieved when structural flexibility is properly taken into account.

AVAILABILITY AND IMPLEMENTATION

Kpax 5.0 may be downloaded for academic use at http://kpax.loria.fr/

CONTACT

dave.ritchie@inria.fr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein flexibility in the light of structural alphabets

Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.

PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins

Posttranslational modifications (PTMs) define covalent and chemical modifications of protein residues. They play important roles in modulating various biological functions. Current PTM databases contain important sequence annotations but do not provide informative 3D structural resource about these modifications. Posttranslational modification structural database (PTM-SD) provides access to structurally solved modified residues, which are experimentally annotated as PTMs. It combines different PTM information and annotation gathered from other databases, e.g. Protein DataBank for the protein structures and dbPTM and PTMCuration for fine sequence annotation. PTM-SD gives an accurate detection of PTMs in structural data. PTM-SD can be browsed by PDB id, UniProt accession number, organism and classic PTM annotation. Advanced queries can also be performed, i.e. detailed PTM annotations, amino acid type, secondary structure, SCOP class classification, PDB chain length and number of PTMs by chain. Statistics and analyses can be computed on a selected dataset of PTMs. Each PTM entry is detailed in a dedicated page with information on the protein sequence, local conformation with secondary structure and Protein Blocks. PTM-SD gives valuable information on observed PTMs in protein 3D structure, which is of great interest for studying sequence-structure- function relationships at the light of PTMs, and could provide insights for comparative modeling and PTM predictions protocols. Database URL: PTM-SD can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/PTM-SD/.

Modeling and molecular dynamics of HPA-1a and -1b polymorphisms: effects on the structure of the β3 subunit of the αIIbβ3 integrin

Background

The HPA-1 alloimmune system carried by the platelet integrin αIIbβ3 is the primary cause of alloimmune thrombocytopenia in Caucasians and the HPA-1b allele might be a risk factor for thrombosis. HPA-1a and -1b alleles are defined by a leucine and a proline, respectively, at position 33 in the β3 subunit. Although the structure of αIIbβ3 is available, little is known about structural effects of the L33P substitution and its consequences on immune response and integrin functions.

Methodology/Principal Findings

A complete 3D model of the L33-β3 extracellular domain was built and a P33 model was obtained by in silico mutagenesis. We then performed molecular dynamics simulations. Analyses focused on the PSI, I-EGF-1, and I-EGF-2 domains and confirmed higher exposure of residue 33 in the L33 β3 form. These analyses also showed major structural flexibility of all three domains in both forms, but increased flexibility in the P33 β3 form. The L33P substitution does not alter the local structure (residues 33 to 35) of the PSI domain, but modifies the structural equilibrium of the three domains.

Conclusions

These results provide a better understanding of HPA-1 epitopes complexity and alloimmunization prevalence of HPA-1a. P33 gain of structure flexibility in the β3 knee may explain the increased adhesion capacity of HPA-1b platelets and the associated thrombotic risk. Our study provides important new insights into the relationship between HPA-1 variants and β3 structure that suggest possible effects on the alloimmune response and platelet function.