Comparison Of 3D Structures Research Articles

Comparison Of 3D Structures Generated by AlphaFold2 to Experimental Structures In Oncogenic Proteins

AlphaFold2 is a machine-learning algorithm that can predict the 3D structure of proteins. 3D protein structures are essential for understanding the function of oncogenic proteins, which can potentially cause cancer. In this study, we compared the structure of 26 oncogenic proteins found experimentally and computationally using AlphaFold2. We used RMSD values to measure how well the AlphaFold2 model fit the experimentally derived protein structures. RMSD values for the oncogenic proteins ranged from 0.204 Å to 1.980 Å with an average of 0.633 Å, showing that AlphaFold2 was a promising tool for predicting the 3D structure of oncogenic proteins. However, we noted that AlphaFold2 has limitations in predicting the structure of highly disordered proteins, proteins with multiple conformations, mutated and artificial proteins, and proteins that are not well-studied experimentally. Our study suggests that AlphaFold2 could be used to identify new targets for cancer treatment and design drugs that can fit into the binding sites of oncogenic proteins. We also hypothesize that AlphaFold2 could be improved by increasing the amount of data available for training, improving the resolution of current data, and using it in conjunction with other protein structure models. We believe that AlphaFold2 is a powerful tool for predicting the structure of oncogenic proteins. Despite the current limitations, we are optimistic that future research will improve AlphaFold2, finding use in cancer research.

Maximizing the Performance of Similarity-Based Virtual Screening Methods by Generating Synergy from the Integration of 2D and 3D Approaches.

Methods for the pairwise comparison of 2D and 3D molecular structures are established approaches in virtual screening. In this work, we explored three strategies for maximizing the virtual screening performance of these methods: (i) the merging of hit lists obtained from multi-compound screening using a single screening method, (ii) the merging of the hit lists obtained from 2D and 3D screening by parallel selection, and (iii) the combination of both of these strategies in an integrated approach. We found that any of these strategies led to a boost in virtual screening performance, with the clearest advantages observed for the integrated approach. On test sets for virtual screening, covering 50 pharmaceutically relevant proteins, the integrated approach, using sets of five query molecules, yielded, on average, an area under the receiver operating characteristic curve (AUC) of 0.84, an early enrichment among the top 1% of ranked compounds (EF1%) of 53.82 and a scaffold recovery rate among the top 1% of ranked compounds (SRR1%) of 0.50. In comparison, the 2D and 3D methods on their own (when using a single query molecule) yielded AUC values of 0.68 and 0.54, EF1% values of 19.96 and 17.52, and SRR1% values of 0.20 and 0.17, respectively. In conclusion, based on these results, the integration of 2D and 3D methods, via a (balanced) parallel selection strategy, is recommended, and, in particular, when combined with multi-query screening.

Single-chain variable fragment antibody-based ic-ELISA for rapid detection of macrolides in porcine muscle and computational simulation of its interaction mechanism

To generate a single-chain fragment variable (scFv) for the detection of macrolide antibiotics (MAs) residues, the biopanning, expression and comparative analysis of four scFvs against MAs generated from five phage display libraries were performed. Three-dimensional (3D) conformation comparative analysis revealed that changes in the active pocket, caused by mutations in the FR framework region, are the main factor affecting the recognition ability of scFv. Homology modeling and molecular docking indicated that the binding sites between the optimal MA-specific scFv candidate DA/3C4/23 and MA are PRO-221, ASP-226, SER-153, PHE-154, ASP-226, and ARG-45. After optimization of soluble expression conditions, stable soluble DA/3C4/23 scFv antibody was obtained, and a 50% inhibition concentration (IC 50 ) of 10.9 μg/L for tylosin (TYL) was achieved. The recoveries of the muscle tissues were 96.73%–107.24% with coefficients of variation below 20%. It indicated that the established method can be effectively applied for food safety monitoring. • Multiple new MAs-specific scFvs were screened from five phage display libraries. • The binding mode between scFv and MAs was first reported. • In-depth comparison of 3D structures between scFvs with different affinities. • The scFv-based ic-ELISA for monitor of MAs in animal tissues was first established.

Structural leitmotif and functional variations of the structural catalytic core in (chymo)trypsin-like serine/cysteine fold proteinases

Proteinases with the (chymo)trypsin-like serine/cysteine fold comprise a large superfamily performing their function through the Acid – Base – Nucleophile catalytic triad. In our previous work (Denesyuk AI, Johnson MS, Salo-Ahen OMH, Uversky VN, Denessiouk K. Int J Biol Macromol. 2020;153:399–411), we described a universal three-dimensional (3D) structural motif, NBCZone, that contains eleven amino acids: dipeptide 42 T–43 T, pentapeptide 54 T–55 T–56 T–57 T(base)–58 T, tripeptide 195 T(nucleophile)–196 T–197 T and residue 213 T (T – numeration of amino acids in trypsin). The comparison of the NBCZones among the members of the (chymo)trypsin-like protease family suggested the existence of 15 distinct groups. Within each group, the NBCZones incorporate an identical set of conserved interactions and bonds. In the present work, the structural environment of the catalytic acid at the position 102 T and the fourth member of the “catalytic tetrad” at the position 214 T was analyzed in 169 3D structures of proteinases with the (chymo)trypsin-like serine/cysteine fold. We have identified a complete Structural Catalytic Core (SCC) consisting of two classes and four groups. The proteinases belonging to different classes and groups differ from each other by the nature of the interaction between their N- and C-terminal β-barrels. Comparative analysis of the 3CLpro(s) from SARS-CoV-2 and SARS-CoV, used as an example, showed that the amino acids at positions 103 T and 179 T affect the nature of the interaction of the “catalytic acid” core (102 T-Core, N-terminal β-barrel) with the “supplementary” core (S-Core, C-terminal β-barrel), which ultimately results in the modulation of the enzymatic activity. The reported analysis represents an important standalone contribution to the analysis and systematization of the 3D structures of (chymo)trypsin-like serine/cysteine fold proteinases. The use of the developed approach for the comparison of 3D structures will allow, in the event of the appearance of new representatives of a given fold in the PDB, to quickly determine their structural homologues with the identification of possible differences.

Comparison of Similar RNA 3D Structures and Substructures Search Tools

ncRNAs functions in the cells has produced exponentially increasing data of RNA 3D structures in both PDB and NDB. Tools are needed to search for similar RNA 3D structures in hope to aid with annotation processes. In this article, five tools (ARTS, FASTR3D, RNA FRABASE 2.0, RAG-3D, and R3D-BLAST2) will be compared based on their performances in analyzing samples of RNA 3D structures in different sizes (pseudoknot, 5S rRNA, and 18S rRNA). ARTS was found to be the most outdated and slow tools, while FASTR3D and RNA FRABASE 2.0 is commonly used as a benchmarking standard. RAG-3D & R3D-BLAST2 produces results with the highest accuracy and is still relevant in the present, however, RAG-3D is found to be limited to analyze RNA 3D structures to a certain size (it cannot process very large samples). This article also provides suggestion for future studies to create tools with similar purposes.

A Novel Way of Comparing Protein 3D Structure Using Graph Partitioning Approach.

Alignment and comparison of protein 3D structures is an important and fundamental task in structural biology to study evolutionary, functional and structural relatedness among proteins. Since two decades, the research on protein structure alignment has been taken up on priority and numbers of research articles are being published. There are incremental advances over previous efforts, and still these methods continue to improve over the time and still this is an open problem in structural biology. A novel methodology has been developed for comparing protein 3D structure by employing conversion of pair of protein 3D structures into 2D graphs (undirected weighted graph), partitioning of 2D graphs into sub-graphs, matching sub-graphs with main graphs and finally these sub-graphs matches calculates similarity between the pair of proteins. The proposed method has been implemented in MATLAB and R Package. The performance of the developed methodology is tested with four existing best methods such as CE, jFATCAT, TM_Align and Dali on 100 proteins benchmark dataset with SCOP database. The proposed method is efficient in terms of time complexity, accuracy, grouping of proteins in relevant structural groups and provides additional information towards non-bonded interactions and sub-graphs indicates the dominance of secondary structure.

Unsupervised Classification of G-Protein Coupled Receptors and Their Conformational States Using IChem Intramolecular Interaction Patterns.

Over the past decade, the ever-growing structural information on G-protein coupled receptors (GPCRs) has revealed the three-dimensional (3D) characteristics of a receptor structure that is competent for G-protein binding. Structural markers are now commonly used to distinguish GPCR functional states, especially when analyzing molecular dynamics simulations. In particular, the position of the sixth helix within the seven transmembrane domains (TMs) is directly related to the coupling of the G-protein. Here, we show that the structural pattern defined by transmembrane intramolecular interactions (hydrogen bonds excluding backbone/backbone interactions, ionic bonds and aromatic interactions) is suitable for comparison of GPCR 3D structures and unsupervised distinction of the receptor states. First, we analyze a microsecond long molecular dynamic simulation of the human ß2-adrenergic receptor (ADRB2). Clustering of the 3D structures by pattern similarity identifies stable states which match the conformational classes defined by structural markers. Furthermore, the method directly spots the few state-specific interactions. Transforming pattern into graph, we extend the method to the comparison of different GPCRs. Clustering all GPCR experimentally determined structures by clique relative size first separates receptors, then their conformational states, thereby suggesting that the interaction patterns are specific of the receptor sequence and that the interaction signatures of conformational states are not shared across distant homologues.

RNA-align: quick and accurate alignment of RNA 3D structures based on size-independent TM-scoreRNA.

Comparison of RNA 3D structures can be used to infer functional relationship of RNA molecules. Most of the current RNA structure alignment programs are built on size-dependent scales, which complicate the interpretation of structure and functional relations. Meanwhile, the low speed prevents the programs from being applied to large-scale RNA structural database search. We developed an open-source algorithm, RNA-align, for RNA 3D structure alignment which has the structure similarity scaled by a size-independent and statistically interpretable scoring metric. Large-scale benchmark tests show that RNA-align significantly outperforms other state-of-the-art programs in both alignment accuracy and running speed. The major advantage of RNA-align lies at the quick convergence of the heuristic alignment iterations and the coarse-grained secondary structure assignment, both of which are crucial to the speed and accuracy of RNA structure alignments. https://zhanglab.ccmb.med.umich.edu/RNA-align/. Supplementary data are available at Bioinformatics online.

Correlation between three-dimensional and cross-sectional characteristics of ideal grain growth: large-scale phase-field simulation study

Grain growth is one of the most fundamental phenomena affecting the microstructure of polycrystalline materials. In experimental studies, three-dimensional (3D) grain growth is usually investigated by examining two-dimensional (2D) cross sections. However, the extent to which the 3D microstructural characteristics can be obtained from cross-sectional observations remains unclear. Additionally, there is some disagreement as to whether a cross-sectional view of 3D grain growth can be fully approximated by 2D growth. In this study, by employing the multi-phase-field method and parallel graphics processing unit computing on a supercomputer, we perform large-scale simulations of 3D and 2D ideal grain growth with approximately three million initial grains. This computational scale supports the detailed comparison of 3D, cross-sectional, and 2D grain structures with good statistical reliability. Our simulations reveal that grain growth behavior in a cross section is very different from those in 3D and fully 2D spaces, in terms of the average and distribution of the grain sizes, as well as the growth kinetics of individual grains. On the other hand, we find that the average grain size in 3D can be estimated as being around 1.2 times that observed in a cross section, which is in good agreement with classical theory in the stereology. Furthermore, based on the Saltykov–Schwartz method, we propose a predictive model that can estimate the 3D grain size distribution from the cross-sectional size distribution.

Jmol SMILES and Jmol SMARTS: specifications and applications.

BackgroundSMILES and SMARTS are two well-defined structure matching languages that have gained wide use in cheminformatics. Jmol is a widely used open-source molecular visualization and analysis tool written in Java and implemented in both Java and JavaScript. Over the past 10 years, from 2007 to 2016, work on Jmol has included the development of dialects of SMILES and SMARTS that incorporate novel aspects that allow new and powerful applications.ResultsThe specifications of “Jmol SMILES” and “Jmol SMARTS” are described. The dialects most closely resemble OpenSMILES and OpenSMARTS. Jmol SMILES is a superset of OpenSMILES, allowing a freer format, including whitespace and comments, the addition of “processing directives” that modify the meaning of certain aspects of SMILES processing such as aromaticity and stereochemistry, a more extensive treatment of stereochemistry, and several minor additions. Jmol SMARTS similarly adds these same modifications to OpenSMARTS, but also adds a number of additional “primitives” and elements of syntax tuned to matching 3D molecular structures and selecting their atoms. The result is an expansion of the capabilities of SMILES and SMARTS primarily for use in 3D molecular analysis, allowing a broader range of matching involving any combination of 3D molecular structures, SMILES strings, and SMARTS patterns. While developed specifically for Jmol, these dialects of SMILES and SMARTS are independent of the Jmol application itself.ConclusionsJmol SMILES and Jmol SMARTS add value to standard SMILES and SMARTS. Together they have proven exceptionally capable in extracting valuable information from 3D structural models, as demonstrated in Jmol. Capabilities in Jmol enabled by Jmol SMILES and Jmol SMARTS include efficient MMFF94 atom typing, conformational identification, SMILES comparisons without canonicalization, identification of stereochemical relationships, quantitative comparison of 3D structures from different sources (including differences in Kekulization), conformational flexible fitting, and atom mapping used to synchronize interactive displays of 2D structures, 3D structures, and spectral correlations, where data are being drawn from multiple sources.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0160-4) contains supplementary material, which is available to authorized users.

Topology independent comparison of RNA 3D structures using the CLICK algorithm

RNA molecules are attractive therapeutic targets because non-coding RNA molecules have increasingly been found to play key regulatory roles in the cell. Comparing and classifying RNA 3D structures yields unique insights into RNA evolution and function. With the rapid increase in the number of atomic-resolution RNA structures, it is crucial to have effective tools to classify RNA structures and to investigate them for structural similarities at different resolutions. We previously developed the algorithm CLICK to superimpose a pair of protein 3D structures by clique matching and 3D least squares fitting. In this study, we extend and optimize the CLICK algorithm to superimpose pairs of RNA 3D structures and RNA–protein complexes, independent of the associated topologies. Benchmarking Rclick on four different datasets showed that it is either comparable to or better than other structural alignment methods in terms of the extent of structural overlaps. Rclick also recognizes conformational changes between RNA structures and produces complementary alignments to maximize the extent of detectable similarity. Applying Rclick to study Ribonuclease III protein correctly aligned the RNA binding sites of RNAse III with its substrate. Rclick can be further extended to identify ligand-binding pockets in RNA. A web server is developed at http://mspc.bii.a-star.edu.sg/minhn/rclick.html.

Conformational flexibility of PL12 family heparinases: structure and substrate specificity of heparinase III from Bacteroides thetaiotaomicron (BT4657).

Glycosaminoglycans (GAGs) are linear polysaccharides comprisedof disaccharide repeat units, a hexuronic acid, glucuronic acid or iduronic acid, linked to a hexosamine, N-acetylglucosamine (GlcNAc) or N-acetylgalactosamine. GAGs undergo further modification such as epimerization and sulfation. These polysaccharides are abundant in the extracellular matrix and connective tissues. GAGs function in stabilization of the fibrillar extracellular matrix, control of hydration, regulation of tissue, organism development by controlling cell cycle, cell behavior and differentiation. Niche adapted bacteria express enzymes called polysaccharide lyases (PL), which degrade GAGs for their nutrient content. PL have been classified into 24 sequence-related families. Comparison of 3D structures of the prototypic members of these families allowed identification of distant evolutionary relationships between lyases that were unrecognized at the sequence level, and identified occurrences of convergent evolution. We have characterized structurally and enzymatically heparinase III from Bacteroides thetaiotaomicron (BtHepIII; gene BT4657), which is classified within the PL12 family. BtHepIII is a 72.5 kDa protein. Wepresent the X-ray structures of two crystal forms of BtHepIII at resolution 1.8 and 2.4 Å. BtHepIII contains two domains, the N-terminal α-helical domain forming a toroid and the C-terminal β-sheet domain. Comparison with recently determined structures of two other heparinases from the same PL12 family allowed us to identify structural flexibility in the arrangement of the domains indicating open-close movement. Based on comparison with other GAG lyases, we identified Tyr301 as the main catalytic residue and confirmed this by site-directed mutagenesis. We have characterized substrate preference of BtHepIII toward sulfate-poor heparan sulfate substrate.

Evolutionary and structural analyses of heterodimeric proteins composed of subunits with same fold.

Heterodimeric proteins with homologous subunits of same fold are involved in various biological processes. The objective of this study is to understand the evolution of structural and functional features of such heterodimers. Using a non-redundant dataset of 70 such heterodimers of known 3D structure and an independent dataset of 173 heterodimers from yeast, we note that the mean sequence identity between interacting homologous subunits is only 23-24% suggesting that, generally, highly diverged paralogues assemble to form such a heterodimer. We also note that the functional roles of interacting subunits/domains are generally quite different. This suggests that, though the interacting subunits/domains are homologous, the high evolutionary divergence characterize their high functional divergence which contributes to a gross function for the heterodimer considered as a whole. The inverse relationship between sequence identity and RMSD of interacting homologues in heterodimers is not followed. We also addressed the question of formation of homodimers of the subunits of heterodimers by generating models of fictitious homodimers on the basis of the 3D structures of the heterodimers. Interaction energies associated with these homodimers suggests that, in overwhelming majority of the cases, such homodimers are unlikely to be stable. Majority of the homologues of heterodimers of known structures form heterodimers (51.8%) and a small proportion (14.6%) form homodimers. Comparison of 3D structures of heterodimers with homologous homodimers suggests that interfacial nature of residues is not well conserved. In over 90% of the cases we note that the interacting subunits of heterodimers are co-localized in the cell.

Structure determination of allo-RA-V and neo-RA-V, RA-series bicyclic peptides from Rubia cordifolia

Two bicyclic hexapeptides, allo-RA-V and neo-RA-V, and one cyclic hexapeptide, O-seco-RA-V were isolated from the roots of Rubia cordifolia. Their groß structures were elucidated on the basis of spectroscopic and X-ray crystallographic analysis. The absolute stereochemistry of allo-RA-V and neo-RA-V were confirmed by their total syntheses, and the absolute stereochemistry of O-seco-RA-V by chemical correlation with deoxybouvardin (RA-V). Comparison of 3D structures highly active RA-VII with less-active peptides, allo-RA-V and neo-RA-V suggests that the orientation of the Tyr-5 and/or Tyr-6 phenyl rings plays a significant role in the biological activity.

LB3D: A Protein Three-Dimensional Substructure Search Program Based on the Lower Bound of a Root Mean Square Deviation Value

Searching for protein structure-function relationships using three-dimensional (3D) structural coordinates represents a fundamental approach for determining the function of proteins with unknown functions. Since protein structure databases are rapidly growing in size, the development of a fast search method to find similar protein substructures by comparison of protein 3D structures is essential. In this article, we present a novel protein 3D structure search method to find all substructures with root mean square deviations (RMSDs) to the query structure that are lower than a given threshold value. Our new algorithm runs in O(m + N/m(0.5)) time, after O(N log N) preprocessing, where N is the database size and m is the query length. The new method is 1.8-41.6 times faster than the practically best known O(N) algorithm, according to computational experiments using a huge database (i.e., >20,000,000 C-alpha coordinates).

CLICK--topology-independent comparison of biomolecular 3D structures.

Our server, CLICK: http://mspc.bii.a-star.edu.sg/click, is capable of superimposing the 3D structures of any pair of biomolecules (proteins, DNA, RNA, etc.). The server makes use of the Cartesian coordinates of the molecules with the option of using other structural features such as secondary structure, solvent accessible surface area and residue depth to guide the alignment. CLICK first looks for cliques of points (3–7 residues) that are structurally similar in the pair of structures to be aligned. Using these local similarities, a one-to-one equivalence is charted between the residues of the two structures. A least square fit then superimposes the two structures. Our method is especially powerful in establishing protein relationships by detecting similarities in structural subdomains, domains and topological variants. CLICK has been extensively benchmarked and compared with other popular methods for protein and RNA structural alignments. In most cases, CLICK alignments were statistically significantly better in terms of structure overlap. The method also recognizes conformational changes that may have occurred in structural domains or subdomains in one structure with respect to the other. For this purpose, the server produces complementary alignments to maximize the extent of detectable similarity. Various examples showcase the utility of our web server.

Biological insights from topology independent comparison of protein 3D structures

Comparing and classifying the three-dimensional (3D) structures of proteins is of crucial importance to molecular biology, from helping to determine the function of a protein to determining its evolutionary relationships. Traditionally, 3D structures are classified into groups of families that closely resemble the grouping according to their primary sequence. However, significant structural similarities exist at multiple levels between proteins that belong to these different structural families. In this study, we propose a new algorithm, CLICK, to capture such similarities. The method optimally superimposes a pair of protein structures independent of topology. Amino acid residues are represented by the Cartesian coordinates of a representative point (usually the Cα atom), side chain solvent accessibility, and secondary structure. Structural comparison is effected by matching cliques of points. CLICK was extensively benchmarked for alignment accuracy on four different sets: (i) 9537 pair-wise alignments between two structures with the same topology; (ii) 64 alignments from set (i) that were considered to constitute difficult alignment cases; (iii) 199 pair-wise alignments between proteins with similar structure but different topology; and (iv) 1275 pair-wise alignments of RNA structures. The accuracy of CLICK alignments was measured by the average structure overlap score and compared with other alignment methods, including HOMSTRAD, MUSTANG, Geometric Hashing, SALIGN, DALI, GANGSTA+, FATCAT, ARTS and SARA. On average, CLICK produces pair-wise alignments that are either comparable or statistically significantly more accurate than all of these other methods. We have used CLICK to uncover relationships between (previously) unrelated proteins. These new biological insights include: (i) detecting hinge regions in proteins where domain or sub-domains show flexibility; (ii) discovering similar small molecule binding sites from proteins of different folds and (iii) discovering topological variants of known structural/sequence motifs. Our method can generally be applied to compare any pair of molecular structures represented in Cartesian coordinates as exemplified by the RNA structure superimposition benchmark.

A structural database for k-turn motifs in RNA

The kink-turn (k-turn) is a common structural motif in RNA that introduces a tight kink into the helical axis. k-turns play an important architectural role in RNA structures and serve as binding sites for a number of proteins. We have created a database of known and postulated k-turn sequences and three-dimensional (3D) structures, available via the internet. This site provides (1) a database of sequence and structure, as a resource for the RNA community, and (2) a tool to enable the manipulation and comparison of 3D structures where known.

The proteolytic system of lactic acid bacteria revisited: a genomic comparison

BackgroundLactic acid bacteria (LAB) are a group of gram-positive, lactic acid producing Firmicutes. They have been extensively used in food fermentations, including the production of various dairy products. The proteolytic system of LAB converts proteins to peptides and then to amino acids, which is essential for bacterial growth and also contributes significantly to flavor compounds as end-products. Recent developments in high-throughput genome sequencing and comparative genomics hybridization arrays provide us with opportunities to explore the diversity of the proteolytic system in various LAB strains.ResultsWe performed a genome-wide comparative genomics analysis of proteolytic system components, including cell-wall bound proteinase, peptide transporters and peptidases, in 22 sequenced LAB strains. The peptidase families PepP/PepQ/PepM, PepD and PepI/PepR/PepL are described as examples of our in silico approach to refine the distinction of subfamilies with different enzymatic activities. Comparison of protein 3D structures of proline peptidases PepI/PepR/PepL and esterase A allowed identification of a conserved core structure, which was then used to improve phylogenetic analysis and functional annotation within this protein superfamily.The diversity of proteolytic system components in 39 Lactococcus lactis strains was explored using pangenome comparative genome hybridization analysis. Variations were observed in the proteinase PrtP and its maturation protein PrtM, in one of the Opp transport systems and in several peptidases between strains from different Lactococcus subspecies or from different origin.ConclusionsThe improved functional annotation of the proteolytic system components provides an excellent framework for future experimental validations of predicted enzymatic activities. The genome sequence data can be coupled to other "omics" data e.g. transcriptomics and metabolomics for prediction of proteolytic and flavor-forming potential of LAB strains. Such an integrated approach can be used to tune the strain selection process in food fermentations.

Chemometric Approach in Quantification of Structural Identity/Similarity of Proteins in Biopharmaceuticals

We present a chemometrics study in which we show the identity or degree of similarity of 3D protein structures of various G-CSF (Granulocyte Colony-Stimulating Factor) isolates. The G-CSF isolates share the same amino acid sequence, but the preparation was carried out by somehow diverse technologies. The comparison of 3D structures was made on the basis of 2D NMR NOESY (Nuclear Overhauser Enhancement Spectroscopy) spectra of proteins. In searching for the most appropriate criteria to determine the identity or degree of similarity of selected spectral regions of different isolates, two methods for quantitative evaluation of identity/similarity were used. The first method compares all peaks in the two investigated protein spectral regions; the extent of peaks that overlap is determined. The second method includes spectral invariants originating from graph theory. The criteria of identity/similarity were calculated from graphs, derived from a collection of up to 200 peaks of investigated 2D NMR spectral region. The peaks were linked into a graph according to the sequential nearest neighborhoods. According to the first method all peaks were relevant, considering that spectral noise was previously removed; the largest similarity was found between the protein of a commercially available G-CSF drug and one of the three new isolates produced in the laboratory. The second method indicated that the pairwise similarity of the three new isolates is larger than the similarity of any of the new isolates with the commercially available drug. This is an expected result taking into account that the new isolates are produced by the same technology, while the commercial product has additives for long-term storage that could not be completely compensated. The proposed measure of similarity may help the developers of biosimilar products to optimize the controllable parameters of the production technology and eventually to argue the identity of the new isolate in comparison with the originator commercial product.