New αIIbß3 variants in 28 Turkish Glanzmann patients; structural hypothesis for complex activation by residues variations in I-EGF domains.

Glanzmann thrombasthenia (GT) is a rare autosomal recessive bleeding disorder characterized by impaired platelet aggregation due to defects in integrin αIIbß3, a fibrinogen receptor. Platelet phenotypes and allelic variations in 28 Turkish GT patients are reported. Platelets αIIbß3 expression was evaluated by flow cytometry. Sequence analyzes of ITGA2B and ITGB3 genes allowed identifying nine variants. Non-sense variation effect on αIIbß3 expression was studied by using transfected cell lines. 3D molecular dynamics (MDs) simulations allowed characterizing structural alterations. Five new alleles were described. αIIb:p.Gly423Asp, p.Asp560Ala and p.Tyr784Cys substitutions impaired αIIbß3 expression. The αIIb:p.Gly128Val substitution allowed normal expression; however, the corresponding NM_000419.3:c.476G>T variation would create a cryptic donor splicing site altering mRNA processing. The ß3:p.Gly540Asp substitution allowed αIIbß3 expression in HEK-293 cells but induced its constitutive activation likely by impairing αIIb and ß3 legs interaction. The substitution alters the ß3 I-EGF-3 domain flexibility as shown by MDs simulations. GT variations are mostly unique although the NM_000419.3:c.1752 + 2 T > C and NM_000212.2:c.1697 G > A variations identified in 4 and 8 families, respectively, might be a current cause of GT in Turkey. MD simulations suggested how some subtle structural variations in the ß3 I-EGF domains might induce constitutive activation of αIIbß3 without altering the global domain structure.

Long-range molecular dynamics show that inactive forms of Protein Kinase A are more dynamic than active forms.

Many protein kinases are characterized by at least two structural forms corresponding to the highest level of activity (active) and low or no activity, (inactive). Further, protein dynamics is an important consideration in understanding the molecular and mechanistic basis of enzyme function. In this work, we use protein kinase A (PKA) as the model system and perform microsecond range molecular dynamics (MD) simulations on six variants which differ from one another in terms of active and inactive form, with or without bound ligands, C-terminal tail and phosphorylation at the activation loop. We find that the root mean square fluctuations in the MD simulations are generally higher for the inactive forms than the active forms. This difference is statistically significant. The higher dynamics of inactive states has significant contributions from ATP binding loop, catalytic loop, and αG helix. Simulations with and without C-terminal tail show this differential dynamics as well, with lower dynamics both in the active and inactive forms if C-terminal tail is present. Similarly, the dynamics associated with the inactive form is higher irrespective of the phosphorylation status of Thr 197. A relatively stable stature of active kinases may be better suited for binding of substrates and detachment of the product. Also, phosphoryl group transfer from ATP to the phosphosite on the substrate requires precise transient coordination of chemical entities from three different molecules, which may be facilitated by the higher stability of the active state.

Multiple interests in structural models of DARC transmembrane protein.

Duffy Antigen Receptor for Chemokines (DARC) is an unusual transmembrane chemokine receptor which (i) binds the two main chemokine families and (ii) does not transduct any signal as it lacks the DRY consensus sequence. It is considered as silent chemokine receptor, a tank useful for chemiotactism. DARC had been particularly studied as a major actor of malaria infection by Plasmodium vivax. It is also implicated in multiple chemokine inflammation, inflammatory diseases, in cancer and might play a role in HIV infection and AIDS. In this review, we focus on the interest to build structural model of DARC to understand more precisely its abilities to bind its physiological ligand CXCL8 and its malaria ligand. We also present innovative development on VHHs able to bind DARC protein. We underline difficulties and limitations of such bioinformatics approaches and highlight the crucial importance of biological data to conduct these kinds of researches.

Protein structure mining using a structural alphabet.

We present a comprehensive evaluation of a new structure mining method called PB-ALIGN. It is based on the encoding of protein structure as 1D sequence of a combination of 16 short structural motifs or protein blocks (PBs). PBs are short motifs capable of representing most of the local structural features of a protein backbone. Using derived PB substitution matrix and simple dynamic programming algorithm, PB sequences are aligned the same way amino acid sequences to yield structure alignment. PBs are short motifs capable of representing most of the local structural features of a protein backbone. Alignment of these local features as sequence of symbols enables fast detection of structural similarities between two proteins. Ability of the method to characterize and align regions beyond regular secondary structures, for example, N and C caps of helix and loops connecting regular structures, puts it a step ahead of existing methods, which strongly rely on secondary structure elements. PB-ALIGN achieved efficiency of 85% in extracting true fold from a large database of 7259 SCOP domains and was successful in 82% cases to identify true super-family members. On comparison to 13 existing structure comparison/mining methods, PB-ALIGN emerged as the best on general ability test dataset and was at par with methods like YAKUSA and CE on nontrivial test dataset. Furthermore, the proposed method performed well when compared to flexible structure alignment method like FATCAT and outperforms in processing speed (less than 45 s per database scan). This work also establishes a reliable cut-off value for the demarcation of similar folds. It finally shows that global alignment scores of unrelated structures using PBs follow an extreme value distribution. PB-ALIGN is freely available on web server called Protein Block Expert (PBE) at http://bioinformatics.univ-reunion.fr/PBE/.

A reduced amino acid alphabet for understanding and designing protein adaptation to mutation.

Protein sequence world is considerably larger than structure world. In consequence, numerous non-related sequences may adopt similar 3D folds and different kinds of amino acids may thus be found in similar 3D structures. By grouping together the 20 amino acids into a smaller number of representative residues with similar features, sequence world simplification may be achieved. This clustering hence defines a reduced amino acid alphabet (reduced AAA). Numerous works have shown that protein 3D structures are composed of a limited number of building blocks, defining a structural alphabet. We previously identified such an alphabet composed of 16 representative structural motifs (5-residues length) called Protein Blocks (PBs). This alphabet permits to translate the structure (3D) in sequence of PBs (1D). Based on these two concepts, reduced AAA and PBs, we analyzed the distributions of the different kinds of amino acids and their equivalences in the structural context. Different reduced sets were considered. Recurrent amino acid associations were found in all the local structures while other were specific of some local structures (PBs) (e.g Cysteine, Histidine, Threonine and Serine for the alpha-helix Ncap). Some similar associations are found in other reduced AAAs, e.g Ile with Val, or hydrophobic aromatic residues Trp with Phe and Tyr. We put into evidence interesting alternative associations. This highlights the dependence on the information considered (sequence or structure). This approach, equivalent to a substitution matrix, could be useful for designing protein sequence with different features (for instance adaptation to environment) while preserving mainly the 3D fold.

"Pinning strategy": a novel approach for predicting the backbone structure in terms of protein blocks from sequence.

The description of protein 3D structures can be performed through a library of 3D fragments, named a structural alphabet. Our structural alphabet is composed of 16 small protein fragments of 5 C alpha in length, called protein blocks (PBs). It allows an efficient approximation of the 3D protein structures and a correct prediction of the local structure. The 72 most frequent series of 5 consecutive PBs, called structural words (SWs)are able to cover more than 90% of the 3D structures. PBs are highly conditioned by the presence of a limited number of transitions between them. In this study, we propose a new method called "pinning strategy" that used this specific feature to predict long protein fragments. Its goal is to define highly probable successions of PBs. It starts from the most probable SW and is then extended with overlapping SWs. Starting from an initial prediction rate of 34.4%, the use of the SWs instead of the PBs allows a gain of 4.5%. The pinning strategy simply applied to the SWs increases the prediction accuracy to 39.9%. In a second step, the sequence-structure relationship is optimized, the prediction accuracy reaches 43.6%.

Protein Block Expert (PBE): a web-based protein structure analysis server using a structural alphabet.

Encoding protein 3D structures into 1D string using short structural prototypes or structural alphabets opens a new front for structure comparison and analysis. Using the well-documented 16 motifs of Protein Blocks (PBs) as structural alphabet, we have developed a methodology to compare protein structures that are encoded as sequences of PBs by aligning them using dynamic programming which uses a substitution matrix for PBs. This methodology is implemented in the applications available in Protein Block Expert (PBE) server. PBE addresses common issues in the field of protein structure analysis such as comparison of proteins structures and identification of protein structures in structural databanks that resemble a given structure. PBE-T provides facility to transform any PDB file into sequences of PBs. PBE-ALIGNc performs comparison of two protein structures based on the alignment of their corresponding PB sequences. PBE-ALIGNm is a facility for mining SCOP database for similar structures based on the alignment of PBs. Besides, PBE provides an interface to a database (PBE-SAdb) of preprocessed PB sequences from SCOP culled at 95% and of all-against-all pairwise PB alignments at family and superfamily levels. PBE server is freely available at http://bioinformatics.univ-reunion.fr/PBE/.

Protein Peeling 2: a web server to convert protein structures into series of protein units.

Protein Peeling 2 (PP2) is a web server for the automatic identification of protein units (PUs) given the 3D coordinates of a protein. PUs are an intermediate level of protein structure description between protein domains and secondary structures. It is a new tool to better understand and analyze the organization of protein structures. PP2 uses only the matrices of protein contact probabilities and cuts the protein structures optimally using Matthews' coefficient correlation. An index assesses the compactness quality of each PU. Results are given both textually and graphically using JMol and PyMol softwares. The server can be accessed from http://www.ebgm.jussieu.fr/~gelly/index.html.

A structural model of a seven-transmembrane helix receptor: the Duffy antigen/receptor for chemokine (DARC).

The Duffy antigen/receptor for chemokine (DARC) is an erythrocyte receptor for malaria parasites (Plasmodium vivax and Plasmodium knowlesi) and for chemokines. In contrast to other chemokine receptors, DARC is a promiscuous receptor that binds chemokines of both CC and CXC classes. The four extracellular domains (ECDs) of DARC are essential for its interaction with chemokines, whilst the first (ECD1) is sufficient for the interaction with malaria erythrocyte-binding protein. In this study, we elaborate and analyze structural models of the DARC. The construction of the 3D models is based on a comparative modeling process and on the use of many procedures to predict transmembrane segments and to detect far homologous proteins with known structures. Threading, ab initio, secondary structure and Protein Blocks approaches are used to build a very large number of models. The conformational exploration of the ECDs is performed with simulated annealing. The second and fourth ECDs are strongly constrained. On the contrary, the ECD1 is highly flexible, but seems composed of three consecutive regions: a small beta-sheet, a linker region and a structured loop. The chosen structural models encompass most of the biochemical features and reflect the known experimental data. They may be used to analyze functional interaction properties.

'Hybrid protein model' for optimally defining 3D protein structure fragments.

MOTIVATION: Our aim is to develop a process that automatically defines a repertory of contiguous 3D protein structure fragments and can be used in homology modeling. We present here improvements to the method we introduced previously: the 'hybrid protein model' (de Brevern and Hazout, THEOR: Chem. Acc., 106, 36-47, (2001)) The hybrid protein learns a non-redundant databank encoded in a structural alphabet composed of 16 Protein Blocks (PBs; de Brevern et al., Proteins, 41, 271-287, (2000)). Every local fold is learned by looking for the most similar pattern present in the hybrid protein and modifying it slightly. Finally each position corresponds to a cluster of similar 3D local folds. RESULTS: In this paper, we describe improvements to our method for building an optimal hybrid protein: (i) 'baby training,' which is defined as the introduction of large structure fragments and the progressive reduction in the size of training fragments; and (ii) the deletion of the redundant parts of the hybrid protein. This repertory of contiguous 3D protein structure fragments should be a useful tool for molecular modeling.

Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks.

By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C(alpha) ("protein blocks"). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into "sequence families" improves the prediction accuracy by 6%. This prediction accuracy exceeds 75% when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (alpha/beta protein) shows that 91% of the sites may be predicted with a prediction accuracy larger than 77% considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling.