PyProtif: a PyMol plugin to retrieve and visualize protein motifs for structural studies.

Proteins often possess several motifs and the ones with similar motifs were found to have similar biochemical properties and thus related biological functions. Thereby, multiple databases were developed to store information on such motifs in proteins. For instance, PDBsum stores the results of Promotif's generated structural motifs and Pfam stores pre-computed patterns of functional domains. In addition to the fact that all this stored information is extremely useful, we can further augment its importance if we ought to integrate these motifs into visualization software. In this work, we have developed PyProtif, a plugin for the PyMOL molecular visualization program, which automatically retrieves protein structural and functional motifs from different databases and integrates them in PyMOL for visualization and analyses. Through an expendable menu and a user-friendly interface, the plugin grants the users the ability to study simultaneously multiple proteins and to select and manipulate each motif separately. Thus, this plugin will be of great interest for structural, evolutionary and classification studies of proteins.

A perspective on the sharing of docking data.

Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.

General Trends of the Camelidae Antibody VHHs Domain Dynamics.

Conformational flexibility plays an essential role in antibodies' functional and structural stability. They facilitate and determine the strength of antigen-antibody interactions. Camelidae express an interesting subtype of single-chain antibody, named Heavy Chain only Antibody. They have only one N-terminal Variable domain (VHH) per chain, composed of Frameworks (FRs) and Complementarity Determining regions (CDRs) like their VH and VL counterparts in IgG. Even when expressed independently, VHH domains display excellent solubility and (thermo)stability, which helps them to retain their impressive interaction capabilities. Sequence and structural features of VHH domains contributing to these abilities have already been studied compared to classical antibodies. To have the broadest view and understand the changes in dynamics of these macromolecules, large-scale molecular dynamics simulations for a large number of non-redundant VHH structures have been performed for the first time. This analysis reveals the most prevalent movements in these domains. It reveals the four main classes of VHHs dynamics. Diverse local changes were observed in CDRs with various intensities. Similarly, different types of constraints were observed in CDRs, while FRs close to CDRs were sometimes primarily impacted. This study sheds light on the changes in flexibility in different regions of VHH that may impact their in silico design.

Quality assessment of VHH models.

Heavy Chain Only Antibodies are specific to Camelid species. Despite the lack of the light chain variable domain, their heavy chain variable domain (VH) domain, named VHH or nanobody, has promising potential applications in research and therapeutic fields. The structural study of VHH is therefore of great interest. Unfortunately, considering the huge amount of sequences that might be produced, only about one thousand of VHH experimental structures are publicly available in the Protein Data Bank, implying that structural model prediction of VHH is a necessary alternative to obtaining 3D information besides its sequence. The present study aims to assess and compare the quality of predictions from different modelling methodologies. Established comparative & homology modelling approaches to recent Deep Learning-based modelling strategies were applied, i.e. Modeller using single or multiple structural templates, ModWeb, SwissModel (with two evaluation schema), RoseTTAfold, AlphaFold 2 and NanoNet. The prediction accuracy was evaluated using RMSD, TM-score, GDT-TS, GDT-HA and Protein Blocks distance metrics. Besides the global structure assessment, we performed specific analyses of Frameworks and CDRs structures. We observed that AlphaFold 2 and especially NanoNet performed better than the other evaluated softwares. Importantly, we performed molecular dynamics simulations of an experimental structure and a NanoNet predicted model of a VHH in order to compare the global structural flexibility and local conformations using Protein Blocks. Despite rather similar structures, substantial differences in dynamical properties were observed, which underlies the complexity of the task of model evaluation.Communicated by Ramaswamy H. Sarma.

Current status of PTMs structural databases: applications, limitations and prospects.

Protein 3D structures, determined by their amino acid sequences, are the support of major crucial biological functions. Post-translational modifications (PTMs) play an essential role in regulating these functions by altering the physicochemical properties of proteins. By virtue of their importance, several PTM databases have been developed and released in decades, but very few of these databases incorporate real 3D structural data. Since PTMs influence the function of the protein and their aberrant states are frequently implicated in human diseases, providing structural insights to understand the influence and dynamics of PTMs is crucial for unraveling the underlying processes. This review is dedicated to the current status of databases providing 3D structural data on PTM sites in proteins. Some of these databases are general, covering multiple types of PTMs in different organisms, while others are specific to one particular type of PTM, class of proteins or organism. The importance of these databases is illustrated with two major types of in silico applications: predicting PTM sites in proteins using machine learning approaches and investigating protein structure-function relationships involving PTMs. Finally, these databases suffer from multiple problems and care must be taken when analyzing the PTMs data.

Shaking the ß-Bulges.

ß-bulges are irregularities inside the ß-sheets. They represent more than 3 percent of the protein residues, i.e., they are as frequent as 3.10 helices. In terms of evolution, ß-bulges are not more conserved than any other local protein conformations within homologous protein structures. In a first of its kind study, we have investigated the dynamical behaviour of ß-bulges using the largest known set of protein molecular dynamics simulations. We observed that more than 50 percent of the existing ß-bulges in protein crystal structures remained stable during dynamics while more than1/6th were not stable at all and disappeared entirely. Surprisingly, 1.1 percent of ß-bulges that appeared remained stable. ß-bulges have been categorized in different subtypes. The most common ß-bulges' types are the smallest insertion in ß-strands (namely AC and AG); they are found as stable as the whole ß-bulges dataset. Low occurring types (namely PC and AS), that have the largest insertions, are significantly more stable than expected. Thus, this pioneer study allowed to precisely quantify the stability of the ß-bulges, demonstrating their structural robustness, with few unexpected cases raising structural questions.

Discrete analyses of protein dynamics.

Protein structures are highly dynamic macromolecules. This dynamics is often analysed through experimental and/or computational methods only for an isolated or a limited number of proteins. Here, we explore large-scale protein dynamics simulation to observe dynamics of local protein conformations using different perspectives. We analysed molecular dynamics to investigate protein flexibility locally, using classical approaches such as RMSf, solvent accessibility, but also innovative approaches such as local entropy. First, we focussed on classical secondary structures and analysed specifically how ß-strand, ß-turns, and bends evolve during molecular simulations. We underlined interesting specific bias between ß-turns and bends, which are considered as the same category, while their dynamics show differences. Second, we used a structural alphabet that is able to approximate every part of the protein structures conformations, namely protein blocks (PBs) to analyse (i) how each initial local protein conformations evolve during dynamics and (ii) if some exchange can exist among these PBs. Interestingly, the results are largely complex than simple regular/rigid and coil/flexible exchange. AbbreviationsNeqnumber of equivalentPBProtein BlocksPDBProtein DataBankRMSfroot mean square fluctuationsCommunicated by Ramaswamy H. Sarma.

iPBAvizu: a PyMOL plugin for an efficient 3D protein structure superimposition approach.

BACKGROUND: Protein 3D structure is the support of its function. Comparison of 3D protein structures provides insight on their evolution and their functional specificities and can be done efficiently via protein structure superimposition analysis. Multiple approaches have been developed to perform such task and are often based on structural superimposition deduced from sequence alignment, which does not take into account structural features. Our methodology is based on the use of a Structural Alphabet (SA), i.e. a library of 3D local protein prototypes able to approximate protein backbone. The interest of a SA is to translate into 1D sequences into the 3D structures. RESULTS: We used Protein blocks (PB), a widely used SA consisting of 16 prototypes, each representing a conformation of the pentapeptide skeleton defined in terms of dihedral angles. Proteins are described using PB from which we have previously developed a sequence alignment procedure based on dynamic programming with a dedicated PB Substitution Matrix. We improved the procedure with a specific two-step search: (i) very similar regions are selected using very high weights and aligned, and (ii) the alignment is completed (if possible) with less stringent parameters. Our approach, iPBA, has shown to perform better than other available tools in benchmark tests. To facilitate the usage of iPBA, we designed and implemented iPBAvizu, a plugin for PyMOL that allows users to run iPBA in an easy way and analyse protein superimpositions. CONCLUSIONS: iPBAvizu is an implementation of iPBA within the well-known and widely used PyMOL software. iPBAvizu enables to generate iPBA alignments, create and interactively explore structural superimposition, and assess the quality of the protein alignments.

A topology-based investigation of protein interaction sites using Hydrophobic Cluster Analysis.

Hydrophobic clusters, as defined by Hydrophobic Cluster Analysis (HCA), are conditioned binary patterns, made of hydrophobic and non-hydrophobic positions, whose limits fit well those of regular secondary structures. They were proved to be useful for predicting secondary structures in proteins from the only information of a single amino acid sequence and have permitted to assess, in a comprehensive way, the leading role of binary patterns in secondary structure preference towards a particular state. Here, we considered the available experimental 3D structures of protein globular domains to enlarge our previously reported hydrophobic cluster database (HCDB), almost doubling the number of hydrophobic cluster species (each species being defined by a unique binary pattern) that represent the most frequent structural bricks encountered within protein globular domains. We then used this updated HCDB to show that the hydrophobic amino acids of discordant clusters, i.e. those less abundant clusters for which the observed secondary structure is in disagreement with the binary pattern preference of the species to which they belong, are more exposed to solvent and are more involved in protein interfaces than the hydrophobic amino acids of concordant clusters. As amino acid composition differs between concordant/discordant clusters, considering binary patterns may be used to gain novel insights into key features of protein globular domain cores and surfaces. It can also provide useful information on possible conformational plasticity, including disorder to order transitions.

In silico prediction of protein flexibility with local structure approach.

Flexibility is an intrinsic essential feature of protein structures, directly linked to their functions. To this day, most of the prediction methods use the crystallographic data (namely B-factors) as the only indicator of protein's inner flexibility and predicts them as rigid or flexible. PredyFlexy stands differently from other approaches as it relies on the definition of protein flexibility (i) not only taken from crystallographic data, but also (ii) from Root Mean Square Fluctuation (RMSFs) observed in Molecular Dynamics simulations. It also uses a specific representation of protein structures, named Long Structural Prototypes (LSPs). From Position-Specific Scoring Matrix, the 120 LSPs are predicted with a good accuracy and directly used to predict (i) the protein flexibility in three categories (flexible, intermediate and rigid), (ii) the normalized B-factors, (iii) the normalized RMSFs, and (iv) a confidence index. Prediction accuracy among these three classes is equivalent to the best two class prediction methods, while the normalized B-factors and normalized RMSFs have a good correlation with experimental and in silico values. Thus, PredyFlexy is a unique approach, which is of major utility for the scientific community. It support parallelization features and can be run on a local cluster using multiple cores.

Investigation of the impact of PTMs on the protein backbone conformation.

Post-translational modifications (PTMs) are known to play a critical role in the regulation of protein functions. Their impact on protein structures and their link to disorder regions have already been spotted in the past decade. Nonetheless, the high diversity of PTM types and the multiple schemes of protein modifications (multiple PTMs, of different types, at different time, etc.) make difficult the direct confrontation of PTM annotations and protein structure data. Therefore, we analyzed the impact of the residue modifications on the protein structures at the local level. Thanks to a dedicated structure database, namely PTM-SD, a large screen of PTMs have been done and analyzed at local protein conformation levels using the structural alphabet protein blocks (PBs). We investigated the relation between PTMs and the backbone conformation of modified residues, of their local environment, and at the level of the complete protein structure. The two main PTM types (N-glycosylation and phosphorylation) have been studied in non-redundant datasets, and then four different proteins were focused, covering three types of PTMs: N-glycosylation in renin endopeptidase and liver carboxylesterase, phosphorylation in cyclin-dependent kinase 2 (CDK2), and methylation in actin. We observed that PTMs could either stabilize or destabilize the backbone structure, at a local and global scale, and that these effects depend on the PTM types.

The respective roles of polar/nonpolar binary patterns and amino acid composition in protein regular secondary structures explored exhaustively using hydrophobic cluster analysis.

Several studies have highlighted the leading role of the sequence periodicity of polar and nonpolar amino acids (binary patterns) in the formation of regular secondary structures (RSS). However, these were based on the analysis of only a few simple cases, with no direct mean to correlate binary patterns with the limits of RSS. Here, HCA-derived hydrophobic clusters (HC) which are conditioned binary patterns whose positions fit well those of RSS, were considered. All the HC types, defined by unique binary patterns, which were commonly observed in three-dimensional (3D) structures of globular domains, were analyzed. The 180 HC types with preferences for either α-helices or ß-strands distinctly contain basic binary units typical of these RSS. Therefore a general trend supporting the "binary pattern preference" assumption was observed. HC for which observed RSS are in disagreement with their expected behavior (discordant HC) were also examined. They were separated in HC types with moderate preferences for RSS, having "weak" binary patterns and versatile RSS and HC types with high preferences for RSS, having "strong" binary patterns and then displaying nonpolar amino acids at the protein surface. It was shown that in both cases, discordant HC could be distinguished from concordant ones by well-differentiated amino acid compositions. The obtained results could, thus, help to complement the currently available methods for the accurate prediction of secondary structures in proteins from the only information of a single amino acid sequence. This can be especially useful for characterizing orphan sequences and for assisting protein engineering and design.

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.

Enzymatic Synthesis of Galactosylated Serine/Threonine Derivatives by ß-Galactosidase from Escherichia coli.

The transgalactosylations of serine/threonine derivatives were investigated using ß-galactosidase from Escherichia coli as biocatalyst. Using ortho-nitrophenyl-ß-D-galactoside as donor, the highest bioconversion yield of transgalactosylated N-carboxy benzyl L-serine benzyl ester (23.2%) was achieved in heptane:buffer medium (70:30), whereas with the lactose, the highest bioconversion yield (3.94%) was obtained in the buffer reaction system. The structures of most abundant galactosylated serine products were characterized by MS/MS. The molecular docking simulation revealed that the binding of serine/threonine derivatives to the enzyme's active site was stronger (-4.6~-7.9 kcal/mol) than that of the natural acceptor, glucose, and mainly occurred through interactions with aromatic residues. For N-tert-butoxycarbonyl serine methyl ester (6.8%) and N-carboxybenzyl serine benzyl ester (3.4%), their binding affinities and the distances between their hydroxyl side chain and the 1'-OH group of galactose moiety were in good accordance with the quantified bioconversion yields. Despite its lower predicted bioconversion yield, the high experimental bioconversion yield obtained with N-carboxybenzyl serine methyl ester (23.2%) demonstrated the importance of the thermodynamically-driven nature of the transgalactosylation reaction.

Expanding the SRI domain family: a common scaffold for binding the phosphorylated C-terminal domain of RNA polymerase II.

The SRI domain is a small three-helix domain originally discovered near the C-terminus of both histone methyltransferase SETD2 and helicase RECQL5. The SRI domain binds to the C-terminal repeat domain of the largest subunit of RNA polymerase II, allowing SETD2 and RECQL5 to regulate various mechanisms associated with RNA transcription. Using original tools to detect common patterns in distantly related sequences, we have identified SRI domains in several additional proteins, most of which are involved in RNA metabolism. Combining sequence analysis with structural prediction, we show that this domain family is more diverse than previously thought and we predict critical structural and functional features.

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/.

ß-Bulges: extensive structural analyses of ß-sheets irregularities.

ß-Sheets are quite frequent in protein structures and are stabilized by regular main-chain hydrogen bond patterns. Irregularities in ß-sheets, named ß-bulges, are distorted regions between two consecutive hydrogen bonds. They disrupt the classical alternation of side chain direction and can alter the directionality of ß-strands. They are implicated in protein-protein interactions and are introduced to avoid ß-strand aggregation. Five different types of ß-bulges are defined. Previous studies on ß-bulges were performed on a limited number of protein structures or one specific family. These studies evoked a potential conservation during evolution. In this work, we analyze the ß-bulge distribution and conservation in terms of local backbone conformations and amino acid composition. Our dataset consists of 66 times more ß-bulges than the last systematic study (Chan et al. Protein Science 1993, 2:1574-1590). Novel amino acid preferences are underlined and local structure conformations are highlighted by the use of a structural alphabet. We observed that ß-bulges are preferably localized at the N- and C-termini of ß-strands, but contrary to the earlier studies, no significant conservation of ß-bulges was observed among structural homologues. Displacement of ß-bulges along the sequence was also investigated by Molecular Dynamics simulations.

Cis-trans isomerization of omega dihedrals in proteins.

Peptide bonds in protein structures are mainly found in trans conformation with a torsion angle ω close to 180°. Only a very low proportion is observed in cis conformation with ω angle around 0°. Cis-trans isomerization leads to local conformation changes which play an important role in many biological processes. In this paper, we reviewed the recent discoveries and research achievements in this field. First, we presented some interesting cases of biological processes in which cis-trans isomerization is directly implicated. It is involved in protein folding and various aspect of protein function like dimerization interfaces, autoinhibition control, channel gating, membrane binding. Then we reviewed conservation studies of cis peptide bonds which emphasized evolution constraints in term of sequence and local conformation. Finally we made an overview of the numerous molecular dynamics studies and prediction methodologies already developed to take into account this structural feature in the research area of protein modeling. Many cis peptide bonds have not been recognized as such due to the limited resolution of the data and to the refinement protocol used. Cis-trans proline isomerization reactions represents a vast and promising research area that still needs to be further explored for a better understanding of isomerization mechanism and improvement of cis peptide bond predictions.

Evolution study of the Baeyer-Villiger monooxygenases enzyme family: functional importance of the highly conserved residues.

Baeyer-Villiger monooxygenases (BVMOs) catalyze the transformation of linear and cyclic ketones into their corresponding esters and lactones by introducing an oxygen atom into a C-C bond. This bioreaction has numerous advantages compared to its chemical version; it does not induce the use of potentially harmful reagents (i.e., green chemistry) and displays significant better enantio- and regio-selectivity. New potential BVMOs were searched using sequence homology for type I BVMO proteins. 116 new sequences were identified as new putative BVMOs respecting the defined selection criteria. Multiple sequence alignments were carried out on the selected sequences to study the conservation of structurally and/or functionally important amino acids during evolution. Type I BVMO signature motif was found to be conserved in 94.8% of the sequences. We noticed also the highly conserved - but previously unnoticed - Threonine 167 (93.1%), located in the signature motif; this position could be added in the pattern used to characterize specific Type I enzymes. Amino acids at the vicinity of the FAD and NADPH cofactors were found also to be highly conserved and the details of the interactions were emphasized. Interestingly, residues at the enzyme binding site were found less conserved in terms of sequence evolution, leading sometimes to some important amino acid changes. These behaviors could explain the enzyme selectivity and specificity for different ligands.