Comprehensive description of protein structures using protein folding shape code

Prediction of folding patterns for intrinsic disordered protein

The conformation flexibility of natural protein causes both complexity and difficulty to understand the relationship between structure and function. The prediction of intrinsically disordered protein primarily is focusing on to disclose the regions with structural flexibility involving relevant biological functions and various diseases. The order of amino acids in protein sequence determines possible conformations, folding flexibility and biological function. Although many methods provided the information of intrinsically disordered protein (IDP), but the results are mainly limited to determine the locations of regions without knowledge of possible folding conformations. Here, the developed protein folding fingerprint adopted the protein folding variation matrix (PFVM) to reveal all possible folding patterns for the intrinsically disordered protein along its sequence. The PFVM integrally exhibited the intrinsically disordered protein with disordering regions, degree of disorder as well as folding pattern. The advantage of PFVM will not only provide rich information for IDP, but also may promote the study of protein folding problem.

Impact of alternating amino acid sequences on beta-amyloid-induced neurotoxicity and neuroinflammation in Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease and the common cause of dementia. The aggregation of beta-amyloid (Aβ peptide) leading to excessive neuroinflammation is considered to be the neuropathological hallmark of AD, although the precise mechanisms remain unclear. Oligomerization of these peptides may be associated with their 42 amino acid residue arrangement. However, the process of amyloid plaque formation is still not well known. The protein folding-shape code (PFSC) method is a powerful tool to analyze protein confirmation which could exhibit the local structural folding features in detail. In our study, we utilized the PFSC to analyze Aβ peptide in humans and mice and found that mouse Aβ42 is less likely to polymerize than human's. Subsequently, we used the PFSC method to analyze the 42 amino acids of Aβ, transformed some species in human Aβ42 and obtained 7 mutants. We showed that it was not easy to aggregate Aβ in mutants. Herein, inflammatory responses were decreased, as indicated by the expression of cytokines. We confirmed that the neurotoxicity of mutant human Aβ was decreased by preventing peptide aggregation. This may represent a new therapeutic approach for treating AD.

Molecular cloning, characterization and 3D modelling of spotted snakehead fbn1 C-terminal region encoding asprosin and expression analysis of fbn1

The FBN1 gene encodes profibrillin protein that is cleaved by the enzyme furin to release fibrillin-1 and a glucogenic hormone, asprosin. Asprosin is implicated in diverse metabolic functions as well as pathological conditions in mammals. However, till date, there are no studies on asprosin in any non-mammalian vertebrate. In this study, we have retrieved the spotted snakehead Channa punctata fbn1 gene (ss fbn1) from the testicular transcriptome data and validated it. The transcript is predicted to encode 2817 amino acid long putative profibrillin protein. Amino acid sequence alignment of deduced ss profibrillin with human profibrillin revealed that the furin cleavage site in profibrillin is well conserved in C. punctata. Further, differential expression of ss fbn1 was observed in various tissues with the highest expression in gonads. Prominent expression of furin was also observed in the gonads suggesting the possibility of proteolytic cleavage of profibrillin protein and secretion of asprosin in C. punctata. In addition, the C-terminal of the fbn1 gene of C. punctata that codes for asprosin protein has been cloned. Using in silico approach, physicochemical properties of the putative ss asprosin were characterized and post-translational changes were predicted. The putative ss asprosin protein sequence is predicted to consist of 142 amino acid residues, with conserved glycosylation sites. Further, the 3D model of ss asprosin was predicted followed by MD (molecular dynamics) simulation for energy minimization. Thus, the current study, for the first time in non-mammalian vertebrates, predicts and characterizes the novel protein asprosin using in silico approach.

High-Throughput Prediction and Design of Novel Conopeptides for Biomedical Research and Development

Cone snail venoms have been considered a valuable treasure for international scientists and businessmen, mainly due to their pharmacological applications in development of marine drugs for treatment of various human diseases. To date, around 800 Conus species are recorded, and each of them produces over 1,000 venom peptides (termed as conopeptides or conotoxins). This reflects the high diversity and complexity of cone snails, although most of their venoms are still uncharacterized. Advanced multiomics (such as genomics, transcriptomics, and proteomics) approaches have been recently developed to mine diverse Conus venom samples, with the main aim to predict and identify potentially interesting conopeptides in an efficient way. Some bioinformatics techniques have been applied to predict and design novel conopeptide sequences, related targets, and their binding modes. This review provides an overview of current knowledge on the high diversity of conopeptides and multiomics advances in high-throughput prediction of novel conopeptide sequences, as well as molecular modeling and design of potential drugs based on the predicted or validated interactions between these toxins and their molecular targets.

Exposing structural variations in SARS-CoV-2 evolution

The mutation of SARS-CoV-2 influences viral function as residue replacements affect both physiochemical properties and folding conformations. Although a large amount of data on SARS-CoV-2 is available, the investigation of how viral functions change in response to mutations is hampered by a lack of effective structural analysis. Here, we exploit the advances of protein structure fingerprint technology to study the folding conformational changes induced by mutations. With integration of both protein sequences and folding conformations, the structures are aligned for SARS-CoV to SARS-CoV-2, including Alpha variant (lineage B.1.1.7) and Delta variant (lineage B.1.617.2). The results showed that the virus evolution with change in mutational positions and physicochemical properties increased the affinity between spike protein and ACE2, which plays a critical role in coronavirus entry into human cells. Additionally, these structural variations impact vaccine effectiveness and drug function over the course of SARS-CoV-2 evolution. The analysis of structural variations revealed how the coronavirus has gradually evolved in both structure and function and how the SARS-CoV-2 variants have contributed to more severe acute disease worldwide.

Exposing Structural Variations in SARS-CoV-2 Evolution

The mutation of SARS-CoV-2 influences viral function as residue replacements affect both physiochemical properties and folding conformations. Although a large amount of data on SARS-CoV-2 is available, the investigation of how viral functions change in response to mutations is hampered by a lack of effective structural analysis. Here, we exploit advances in protein structure fingerprint technology to study the folding conformational changes induced by mutations. With the integration of both protein sequences and folding conformations and alignments of SARS-CoV to SARS-CoV-2, the UK variant and India variant, we found that structural variations in the spike protein at the binding interface interacting with ACE2 play a critical role in coronavirus entry into human cells. Additionally, the structural variations impact vaccine effectiveness and drug function over the course of SARS-CoV-2 evolution. The analysis of structural variations revealed how the coronavirus has gradually evolved in both structure and function and how the SARS-CoV-2 variants have contributed to more severe acute disease worldwide.

Characterization and validation of somatic mutation spectrum to reveal heterogeneity in gastric cancer by single cell sequencing

Gastric cancer (GC) is a highly heterogeneous disease with multiple cellular types and poor prognosis. However, the cellular evolution and molecular basis of GC at the individual intra-tumor level has not been well demonstrated. We performed single-cell whole exome sequencing to detect somatic single-nucleotide variants (SNVs) and significantly mutated genes (SMGs) among 34 tumor cells and 9 normal cells from a patient with GC. The Complete Prediction for Protein Conformation (CPPC) approach directly predicting the folding conformation of the protein 3D structure with Protein Folding Shape Code, combined with functional experiments were used to confirm the characterization of mutated SMGs in GC cells. We identified 201 somatic SNVs, including 117 non-synonymous mutations in GC cells. Further analysis identified 24 significant mutated genes (SMGs) in single cells, for which a single amino acid change might affect protein conformation. Among them, two genes (CDC27 and FLG) that were mutated only in single cells but not in the corresponding tumor tissue, were recurrently present in another GC tissue cohort, and may play a potential role to promote carcinogenesis, as confirmed by functional characterization. Our findings showed a mutational landscape of GC at intra-tumor level for the first time and provided opportunities for understanding the heterogeneity and individualized target therapy for this disease.

NAHAL-Flex: A Numerical and Alphabetical Hinge Detection Algorithm for Flexible Protein Structure Alignment

Flexible proteins are proteins that have conformational changes in their structures. Protein flexibility analysis is critical for classifying and understanding protein functionality. For that analysis, the hinge areas where proteins show flexibility must be detected. To detect the location of the hinges, previous methods have utilized the three-dimensional (3D) structure of proteins, which is highly computational. To reduce the computational complexity, this study proposes a novel text-based method using structural alphabets (SAs) for detecting the hinge position, called NAHAL-Flex. Protein structures were encoded to a particular type of SA called the protein folding shape code (PFSC), which remains unaffected by location, scale, and rotation. The flexible regions of the proteins are the only places in which letter sequences can be distorted. With this knowledge, it is possible to find the longest alignment path of two letter sequences using a dynamic programming (DP) algorithm. Then, the proposed method looks for regions where the alphabet sequence is distorted to find the most probable hinge positions. In order to reduce the number of hinge positions, a genetic algorithm (GA) was utilized to find the best candidate hinge points. To evaluate the method's effectiveness, four different flexible and rigid protein databases, including two small datasets and two large datasets, were utilized. For the small dataset, the NAHAL-Flex method was comparable to state-of-the-art structural flexible alignment methods. The result for the large datasets show that NAHAL-Flex outperforms some well-known alignment methods, e.g., DaliLite, Matt, DeepAlign, and TM-align; the speed of NAHAL-Flex was faster and its result was more accurate than the other methods.

Cone Snails: A Big Store of Conotoxins for Novel Drug Discovery

Marine drugs have developed rapidly in recent decades. Cone snails, a group of more than 700 species, have always been one of the focuses for new drug discovery. These venomous snails capture prey using a diverse array of unique bioactive neurotoxins, usually named as conotoxins or conopeptides. These conotoxins have proven to be valuable pharmacological probes and potential drugs due to their high specificity and affinity to ion channels, receptors, and transporters in the nervous systems of target prey and humans. Several research groups, including ours, have examined the venom gland of cone snails using a combination of transcriptomic and proteomic sequencing, and revealed the existence of hundreds of conotoxin transcripts and thousands of conopeptides in each Conus species. Over 2000 nucleotide and 8000 peptide sequences of conotoxins have been published, and the number is still increasing quickly. However, more than 98% of these sequences still lack 3D structural and functional information. With the rapid development of genomics and bioinformatics in recent years, functional predictions and investigations on conotoxins are making great progress in promoting the discovery of novel drugs. For example, ω-MVIIA was approved by the U.S. Food and Drug Administration in 2004 to treat chronic pain, and nine more conotoxins are at various stages of preclinical or clinical evaluation. In short, the genus Conus, the big family of cone snails, has become an important genetic resource for conotoxin identification and drug development.

mulPBA: an efficient multiple protein structure alignment method based on a structural alphabet

The increasing number of available protein structures requires efficient tools for multiple structure comparison. Indeed, multiple structural alignments are essential for the analysis of function, evolution and architecture of protein structures. For this purpose, we proposed a new web server called multiple Protein Block Alignment (mulPBA). This server implements a method based on a structural alphabet to describe the backbone conformation of a protein chain in terms of dihedral angles. This 'sequence-like' representation enables the use of powerful sequence alignment methods for primary structure comparison, followed by an iterative refinement of the structural superposition. This approach yields alignments superior to most of the rigid-body alignment methods and highly comparable with the flexible structure comparison approaches. We implement this method in a web server designed to do multiple structure superimpositions from a set of structures given by the user. Outputs are given as both sequence alignment and superposed 3D structures visualized directly by static images generated by PyMol or through a Jmol applet allowing dynamic interaction. Multiple global quality measures are given. Relatedness between structures is indicated by a distance dendogram. Superimposed structures in PDB format can be also downloaded, and the results are quickly obtained. mulPBA server can be accessed at www.dsimb.inserm.fr/dsimb_tools/mulpba/ .

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

Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.

iPBA: a tool for protein structure comparison using sequence alignment strategies

With the immense growth in the number of available protein structures, fast and accurate structure comparison has been essential. We propose an efficient method for structure comparison, based on a structural alphabet. Protein Blocks (PBs) is a widely used structural alphabet with 16 pentapeptide conformations that can fairly approximate a complete protein chain. Thus a 3D structure can be translated into a 1D sequence of PBs. With a simple Needleman–Wunsch approach and a raw PB substitution matrix, PB-based structural alignments were better than many popular methods. iPBA web server presents an improved alignment approach using (i) specialized PB Substitution Matrices (SM) and (ii) anchor-based alignment methodology. With these developments, the quality of ∼88% of alignments was improved. iPBA alignments were also better than DALI, MUSTANG and GANGSTA⁺ in >80% of the cases. The webserver is designed to for both pairwise comparisons and database searches. Outputs are given as sequence alignment and superposed 3D structures displayed using PyMol and Jmol. A local alignment option for detecting subs-structural similarity is also embedded. As a fast and efficient ‘sequence-based’ structure comparison tool, we believe that it will be quite useful to the scientific community. iPBA can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/ipba/.

Improvement of protein structure comparison using a structural alphabet

The three dimensional structure of a protein provides major insights into its function. Protein structure comparison has implications in functional and evolutionary studies. A structural alphabet (SA) is a library of local protein structure prototypes that can abstract every part of protein main chain conformation. Protein Blocks (PBs) is a widely used SA, composed of 16 prototypes, each representing a pentapeptide backbone conformation defined in terms of dihedral angles. Through this description, the 3D structural information can be translated into a 1D sequence of PBs. In a previous study, we have used this approach to compare protein structures encoded in terms of PBs. A classical sequence alignment procedure based on dynamic programming was used, with a dedicated PB Substitution Matrix (SM). PB-based pairwise structural alignment method gave an excellent performance, when compared to other established methods for mining. In this study, we have (i) refined the SMs and (ii) improved the Protein Block Alignment methodology (named as iPBA). The SM was normalized in regards to sequence and structural similarity. Alignment of protein structures often involves similar structural regions separated by dissimilar stretches. A dynamic programming algorithm that weighs these local similar stretches has been designed. Amino acid substitutions scores were also coupled linearly with the PB substitutions. iPBA improves (i) the mining efficiency rate by 6.8% and (ii) more than 82% of the alignments have a better quality. A higher efficiency in aligning multi-domain proteins could be also demonstrated. The quality of alignment is better than DALI and MUSTANG in 81.3% of the cases. Thus our study has resulted in an impressive improvement in the quality of protein structural alignment.

PSS-SQL: protein secondary structure - structured query language

Secondary structure representation of proteins provides important information regarding protein general construction and shape. This representation is often used in protein similarity searching. Since existing commercial database management systems do not offer integrated exploration methods for biological data e.g. at the level of the SQL language, the structural similarity searching is usually performed by external tools. In the paper, we present our newly developed PSS-SQL language, which allows searching a database in order to identify proteins having secondary structure similar to the structure specified by the user in a PSS-SQL query. Therefore, we provide a simple and declarative language for protein structure similarity searching.

Discrete-continuous duality of protein structure space

Recently, the nature of protein structure space has been widely discussed in the literature. The traditional discrete view of protein universe as a set of separate folds has been criticized in the light of growing evidence that almost any arrangement of secondary structures is possible and the whole protein space can be traversed through a path of similar structures. Here we argue that the discrete and continuous descriptions are not mutually exclusive, but complementary: the space is largely discrete in evolutionary sense, but continuous geometrically when purely structural similarities are quantified. Evolutionary connections are mainly confined to separate structural prototypes corresponding to folds as islands of structural stability, with few remaining traceable links between the islands. However, for a geometric similarity measure, it is usually possible to find a reasonable cutoff that yields paths connecting any two structures through intermediates.

Advances and pitfalls of protein structural alignment

Structure comparison opens a window into the distant past of protein evolution, which has been unreachable by sequence comparison alone. With 55,000 entries in the Protein Data Bank and about 500 new structures added each week, automated processing, comparison, and classification are necessary. A variety of methods use different representations, scoring functions, and optimization algorithms, and they generate contradictory results even for moderately distant structures. Sequence mutations, insertions, and deletions are accommodated by plastic deformations of the common core, retaining the precise geometry of the active site, and peripheral regions may refold completely. Therefore structure comparison methods that allow for flexibility and plasticity generate the most biologically meaningful alignments. Active research directions include both the search for fold invariant features and the modeling of structural transitions in evolution. Advances have been made in algorithmic robustness, multiple alignment, and speeding up database searches.