Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B15/00—ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B15/00—ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment
  • G16B15/20—Protein or domain folding
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
  • G16B20/30—Detection of binding sites or motifs
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B45/00—ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
  • G16C20/50—Molecular design, e.g. of drugs

Definitions

• the present invention pertains to the field of structural biology and relates to a process to compare various three-dimensional structures and to identify functional similarities among them.
• this process applies to macromolecules, as for example proteins.
• Profile analysis [4] is a technique based on multiple sequence alignments of homologous sequences and may be used to test a sequence for its membership of a family.
• Pattinprot [5] allows to search a database for any given pattern, that may have been inferred from multiple sequence alignments such as those obtained with ClustalW [ 6 ] from a set of homologous protein sequences .
• tools such as the Dali/FSSP server [7, 8] that mainly use the main chain to find similarities and classify proteins. But these process reach their limits in many cases: a significant similarity in the sequence or in the fold of two proteins is neither necessary nor sufficient to prove that they share a common biological function.
• the applicant has now developed a new process of identification of similarities between the 3D atomic structures, even if those similarities are not exposed over the surface of said 3D atomic structures.
• the process of comparison of 3D atomic structures of the invention is based on the comparisons of defined chemical groups onto the 3D atomic structures and allows the detection of local similarities even when neither the fold nor sequence for example aminoacid sequences for polypeptides sequences or nucleotide sequences for nucleic acid sequences are conserved.
• This process requires the attribution of selected physico- chemical parameters to each atom of a 3D atomic structure, then the representation of each 3D atomic structure by a graph of chemical groups, i.e. if said chemical groups are selected by forming triplets, then the graph is represented by triangles .
• the starting point of the representation of 3D atomic structures is the definition of chemical groups within the structure. Some atoms may not belong to any of the chemical groups, whereas some others may be part of several groups as illustrated by table 1. Table 1 illustrates the definition of some chemical groups that may be used in the process of the invention, though other definitions could be specified by the user.
• the chemical group description groups done in this table 1 show an example of correspondence between chemical groups and aminoacids.
• Column 3 shows amino acids that contain at least one of the given chemical group from column 1.
• Column 4 indicates the geometric construction that is associated to the given chemical group, as defined in Figure 3.
• the 3D atomic structures Before any comparison, the 3D atomic structures have to be preformatted. This operation takes usually longer than the comparison itself. Thus, the preformatted data may be stored into a database in order to be reused later.
• Chemical groups are defined as sets of atoms that share strong geometric constraints and in a way that focuses on their potential interaction with target molecules .
• Hydrogen bond donors hydrogen bond acceptors, aromatic rings are examples of potential interactors with target molecules and thus may constitute chemical groups within the meaning of the present invention.
• chemical groups are 3D atomic substructures having a common set of physico-chemical parameters including:
• Additional geometric information is associated to each chemical group.
• the form of this information is specific to each kind of chemical group, since it is only required for the comparison and the scoring of chemical groups of the same kind.
• SI empty information. This could be used to represent isotropic objects such as a charge.
• S2 non oriented symmetry axis . This could be used to reflect symmetric bipolar objects such as aromatic rings.
• S3 simple polarisation. This represents an orientation in a single direction This representation may be useful to represent hydrogen bond donors and acceptors.
• S4 semi-symmetric double polarisation . This is an oriented object like S3 in which the perfect symmetry around the axis is replaced with a 2-order symmetry around the axis. S4 could be used to represent carboxylic groups in their basic form.
• S5 double polarisation .
• S5 may be used to represent objects with no symmetry axis such as amide groups .
• Geometrical contructs Si, S2, S3, S4 and S5 are defined using vectors in addition to the spatial position of the chemical groups, as illustrated in figure 3.
• Chemical groups, according to the invention are independent from the comparison algorithms and thus may be changed according to the user ' s requirements .
• the parameter called "local density" D is calculated for each atom A occupying a spatial position P
• the burial of atoms may be estimated using a continuous local atomic density function.
• the general expression of a local density D(x p , y P , z p ) around the position P is:
• x, y and z are spatial coordinates
• m is the density function
• w is a weight function to reduce the influence of the peripheral atoms around a given one.
• r is x 2 +y 2 + z 2 and r c a critical radius.
• Factor [1/4] allows to make r independent from r c if m is constant.
• the burial of a given chemical group may then be estimated by two alternative means: a) by calculating the arithmetic mean of the local atomic densities around each atom belonging to this group. b) by using the local atomic density for the position of its center.
• a vector that indicates the exterior of the 3D atomic structure is computed.
• This vector indicating the exterior of the 3D atomic structure may be represented by a density gradient.
• Vector CP wherein point C is the local center of mass of atom A occupying a position P may be used.
• the local center of mass C(P) for point P is a point which cartesian coordinates (x c , y c , z c ) match the following formulation:
• the weight function w may be defined similarly to the weight function used in the local density expression defined in section 1.1.
• each atom A occupying the position P and associated to the local center of mass C is done by the vector CR that points towards the exterior of the 3D atomic structure.
• the notion of exterior depends on the weight function that has been adopted for the calculation of the center of mass.
• a critical radius r. ranging from 3 to 50 A, preferably from 5 to 20 A is used.
• Every chemical group of the 3D atomic structure consists of a set of atoms as defined in said input file (figure 2).
• the mean position P of these atoms, the mean position C of the local centers of mass and the mean local density D are computed and recorded.
• This step reduces the representation of the 3D atomic structure by a set of chemical groups instead of atoms (figure 4A) .
• 1.4- Further selection of chemical groups At this stage, a selection over the chemical groups is performed (figure 4B).
• the following procedures allow the user to select specific parts of the molecular structure: a ) automatic selection of most exposed chemical groups using a local density function and a threshold, b) semi-automatic selection of chemical groups that are possibly interacting with a given set of chemical groups, c ) manual selection of subsets of chemical groups .
• Step (b) is based on the definition for each chemical group of a set of points called virtual interactors .
• a chemical group in a given molecule is here denoted ( P, L) where P is its position and L is the set of points constituting the virtual interactors.
• a given group Pi / Li) is said to be interacting with ( P, L) if and only if there exists at least one point Q belonging to L t such that PQ ⁇ d ⁇ , where d max is an empirical threshold.
• two virtual interactors can be defined for aromatic rings, each of them being located symmetrically on both sides of the aromatic ring at a distance of 4 A of its center.
• the sets of chemical groups are selected in such a way that they comprise three chemical groups (triplets). Then, the sets of chemical groups that represent the 3D atomic structure are converted to triangles of chemical groups. In this particular embodiment of the invention, each triplet (A, B, C) of chemical group is rejected if the distance between the physical position of two groups among A, B, and C is higher or lower than given distance thresholds.
• Sets of chemical groups are oriented against the surface, for instance, in the particular embodiment of sets of three chemical groups, the orientation of a triangle (Pi; P2; P3) of chemical groups is estimated, for example, by using the scalar triple product of ctT , CP 2 a d CP 3 , wherein C is the centre of the local centres of mass of PI, P2 and P3.
• ⁇ each vertex represents one triangle
• each edge represent the adjacency of two triangles. Also, additional parameters may be added to this graph. For instance, the angle between the adjacent triangles is associated to each edge in the graph.
• the orientation of the triangle towards the rest of the 3D atomic structure is estimated by the scalar triple product of ( C ⁇ , c 2 , cf, ) , C being the local center of mass of the triad, i.e. the center of C 7 , C 2 and C 3 which are the local centers of mass of the chemical groups located at P ; , P 2 and P 3 .
• the final representation of the 3D atomic structure is obtained by connecting adjacent triangles, i.e. triangles that share exactly two chemical groups, to make a graph in which each triangle forms a vertex (figure 4D).
• the process of comparison of two 3D atomic structures comprises the steps hereafter described : 3.1 Searching of pairs of similar clusters of chemical groups, (i.e. triplets).
• the criteria for pair similarity are various and can be selected among the group consisting of: (a) same kind of chemical groups,
• the criteria for selecting a given pair of clusters are the following: a) every score associated to a given parameter must be above a given threshold; said threshold being either constant or dependent on the type of chemical group or the environment.
• a parameter has been designated to compare each chemical group, for example, aromatic group and guanidium group correspond both to a « bipolar » construction, but the first one has an « angle » parameter with a value of 60 degrees and the second one has an « angle » parameter with a value of 45 degrees.
• the global score for the pair of clusters must be above a given threshold. Said global score combines individual scores obtained at step (a), by using a linear combination of these scores.
• Thresholds for individual scores at step (a) or global score at step (b) may be also designed empirically, possibly using automated optimizations based on statistical studies .
• each pair of similar triplets forms a vertex in a comparison graph.
• T 2rl and T 2,2 must be connected (adjacent) in M 2 ;
• the angle between T lfl and T lf2 must be similar to the angle between T 2fl and T 2f2
• the independent subgraphs in the comparison graph for the 3D atomic structures M x and M 2 represent then a set of pairs of equivalent triangles corresponding to two structurally equivalent regions in 3D atomic structures M and M 2 .
• the pairs of similar triangles are converted into pairs of chemical groups.
• Such sets of pairs of converted chemical groups are hereafter called patches.
• Pairs of chemical groups within a patch may be superposed by minimizing a distance function.
• the distance function is the following: D(g2) ⁇ + ⁇ ⁇ orient(g g 2 ) where pos (g) is the position of the chemical group g after optimal superimposition of the given set of pairs, and D ( g) its local density; ori nt(g ,g 2 ) is the difference of orientation between chemical groups g ⁇ and g 2 after optimal superimposition.
• ⁇ and ⁇ are weighting coefficients that are defined on an empirical basis.
• the definitive patches are known among others.
• An additional parameter called atomic volume difference is computed for each patch and is used as an additional criterion for: a ) eliminating patches with low shape similarity, b) building a more relevant score.
• the purpose of the computation of the atomic volume difference is to compare the volumes of three kinds of atoms for a given patch:
• V x atoms surrounding chemical groups in cluster (3D atomic substructure) extracted from M x ;
• V 2 atoms surrounding chemical groups in cluster
• V atoms surrounding chemical groups in both clusters after superposition of the said clusters.
• V x and V 2 are similar, and if V is similar to
• V x and V 2 are similar, but V is much higher than V or V 2 , then the repartition of the atoms around the selected chemical groups is much different.
• a score that represents atomic volume difference is derived from these calculations .
• the 3D substructures of the 3D atomic structures are compared in order to obtain a list of similar 3D substructures that are associated to a score that combines several of the following criteria on an empirical basis : a) deviation after optimal superimposition, b) average difference of local density between chemical groups, c ) difference in the orientation of the superimposed chemical groups, using a specific scoring function for each kind of chemical group, d) volume of the chemical groups, e) difference in the shape of the patches by means of an atomic volume difference calculation
• a clique is a complete subgraph from a given graph.
• a ⁇ - clique C in a given graph G is a subgraph such that every vertex from C is connected to at least /l-(
• clique is a synonym for 1-clique.
• a maximal clique is a clique which is not a subset of any other clique.
• a maximal ⁇ -clique is a ⁇ -clique which is not a subset of any other ⁇ -clique.
• M t comprising a set of chemical groups:
• comparison( ., . ) ?., . , ⁇ ,B,ji,...,R, ⁇
• P is defined as follows: I l ⁇ y ' ⁇ m and ⁇ /tecomparison( ( , ⁇
• the method of the invention comprises the construction of a graph G t consisting of: a) vertices that match the elements of P ( , b) edges that connect any vertices u and v sufficiently overlapping according to a given predicate such as the following:
• overlap is a real number within the range 0 and 1,preferably between 0,5 and 1 and more preferably 0,7.
• J maximal ⁇ clic l ues ( P ') ⁇ l b) there is an edge connecting vertices V ⁇ , a and Vj,_. if and only if :
• a cluster V of chemical groups is a set of overlapping sets of chemical groups.
• V can be converted into a set ⁇ (g ⁇ ,w ⁇ ),(g2,w_),... ⁇ where each chemical group g l is associated to a weight w t that may range from 1 to I v
• the problem of finding the maximum clique in an arbitrary graph i.e. the maximal clique of maximum cardinality, is known to be NP-complete . Therefore, the problem of getting all maximal cliques and the larger problem of getting all maximal ⁇ -cliques are also NP- complete .
• the algorithm that is used for finding all maximal ⁇ -cliques is a clustering algorithm: subgraphs are initially constituted by individual vertices and are extended using neighbor vertices under the condition that the resulting subgraph is still a ⁇ -clique. Redundant subgraphs are removed during the process.
• Database containing 3D atomic substructures of 3D structures may be preformatted to allow a quick comparison of one of its members to any other 3D structure preformatted.
• the design of said databases can be applied to any kind of 3D atomic structures such as proteins, nucleic acids or other natural and artificial polymers, but also non-polymeric atomic 3D structures.
• the database may comprise complete 3D atomic substructures but also fragments thereof.
• the database may comprise particular fragments of 3D atomic structures implicated in biological processes, such as enzymatic processes, reversible or irreversible binding of a class of molecules, sensitivity to a particular physico-chemical environment, energy conversion, self modification, antigenicity, modification of the intensity of a biological process.
• the fragments of 3D atomic substructures to be included in the database may also be determined automatically, by selection of chemical groups that interact with a ligand which is present in the 3D atomic structure or predicted by biochemical experiences .
• ligand As ligand one may understand a 3D atomic structure, whatever is its nature, such as a peptide or an oligonucleotide, able to bind to another molecular partner, as a receptor, an antibody, a co-factor.
• the selection of the sites of interaction between the 3D atomic structure and the ligand may be made by characterizing positioning around each chemical group included in essential regions of the 3D atomic structure.
• each chemical group is associated to a set of target positions.
• a target position is a spatial position with high probability for finding a molecular interactor.
• the number of target positions for a given chemical group may depend on its chemical environment.
• the present invention relates to a process to identify similar 3D substructures onto 3D atomic structures having a plurality of individual atoms, performed with the aid of a programmed computer comprising the steps of: a) attributing to each individual atom of said 3D atomic structure a structural parameter combining its atomic local density D, its local center of mass C and its orientation in relation with its position P. b) constructing chemical groups by setting individual atoms having similar structural parameters, c) constructing clusters of at least three chemical groups by setting said chemical groups whose reciprocal distances are constrained, d) comparing clusters constructed at step (c) and identifying said clusters sharing similar 3D structures .
• the atomic local density D of step (a) may be calculated for each atom A, on basis to its spatial position P defined by its coordinates (x p , y p , z p ) as a function of its density m, modulated by a weight function w, by means of the function:
• weight function w is preferably calculated as a spherical function
• r is Jx 2 +y 2 + ⁇ 2 , r. a critical radius and the factor [1/4] allows to make r independent from r. if m is constant.
• x, y and z are spatial coordinates, m is the density function and w is a weight function.
• each atom A occupying a position P and having a local center of mass C(P) at step (a) of the process may be preferably calculated by means of a density gradient represented by a vector c .
• the reciprocal distances between chemical groups selected in step (c) are comprised between 2 to 20 A, preferably between 5 to 12 A.
• the construction of clusters at step (c) comprises orientation of said clusters against the 3D atomic structure and preferably this orientation is operated by means of a scalar triple product of three vectors CP, , CP j r CP k wherein C is the center of the local centers of mass of each chemical group and P i5 P j5 P k are three distinct points in the cluster.
• the process to identify similar 3D substructures is particular useful to construct clusters of chemical groups chat can be are further stored and classified in a database.
• the comparison of a given pair of clusters at step (d) of a such process to identify similar 3D substructures comprises the identification of at least one structural similarity selected from the group consisting of:
• the process comprises the calculation of a global score , that may be calculated as a function combining several parameters indicating the 10 similarity of said clusters, said parameters being selected among the group consisting of :
• the process may be applied to the capability of binding flexible ligands using the same kind of weak chemical bonds.
• the capability may be estimated by analysis of deviation of short range distances between chemical groups.
• the resemblance between atomic environment comprises the calculation of a score by comparing volumes of atoms around each converted pair of chemical groups and a such score is calculated by means of the function:
• V x is the volume of atoms surrounding chemical groups in cluster (3D atomic substructure) extracted from atomic structure M 1 ;
• V 2 is the volume of atoms surrounding chemical groups in cluster (3D atomic substructure) extracted from atomic structure M 2 ;
• V is the volume of atoms surrounding chemical groups in both clusters after superposition of the said clusters.
• the process to identify similar 3D substructures of the present invention may comprise before the step (c) a further step comprising the restriction of constructed chemical groups.
• Said restriction of constructed chemical groups may be operated by means of an additional step (f) comprising the selection of chemical groups at locations where the local atomic density is below a definite threshold.
• said restriction may be operated by an additional step (g) wherein the restriction of constructed chemical groups is operated by means of a selection among :
• the process to identify similar 3D substructures of the present invention may be performed by applying a refinement step comprising : i) converting the pairs of clusters identified at step (d) to pairs of chemical groups, ii) minimizing the reciprocal distances between converted pairs of chemical groups by means of a distance function.
• the process to identify 3D similar substructures according invention may further comprise a step (e) of clustering pairs of clusters identified at step (d) into a larger pair of clusters sharing similar 3D structures.
• the 3D atomic structure having a plurality of individual atoms is a covalent or weak assembly of at least one molecule selected among the group comprising: natural and artificial proteins, oligopeptides , polypeptides, nucleic acids, natural and artificial oligonucleotides, natural and artificial oligosaccharides and polysaccharides, glycoproteins , lipoproteins, lipids, ions, water, natural and synthetic polymers, non polymeric structures, natural and artificial inorganic molecules.
• the 3D atomic substructure to be identified onto 3D atomic structures having a plurality of individual atoms is a functional site.
• Said functional site may be selected among the group consisting of: enzymatic active sites, sites of reversible or irreversible binding of specific classes of molecules, sites sensitive to physico-chemical changes in the environment, chemical groups involved in energy conversion, self modification locations, antigenic parts of a molecule, mimetic sites, consensus sites, highly variable sites, sites necessary for initiating or interrupting a biological pathway, sites with particular physico-chemical properties, sites with particular chemical composition, protein taxons, immunoglobulin domains, DNA consensus sequences, gene expression signals, promoter elements, RNA processing signals, translational initiation sites, recognition motifs of a large variety of sequence-specific DNA-binding proteins, protein and nucleic acid compositional domains, glutamine-rich activation domains, CpG island, interaction site between a protein and a ligand, functional sugar binding site.
• enzymatic active sites sites of reversible or irreversible binding of specific classes of molecules, sites sensitive to physico-chemical changes in the environment, chemical groups involved in
• the 3D atomic substructures to be identified onto 3D atomic structures having a plurality of individual atoms may be 3D structural sites issued from combinatorial, or conventional screening.
• the invention relates also to a process to predict functional sites onto 3D atomic structures comprising the identification of 3D atomic substructures by means of a process according to the invention, further comprising the correlation of said identified 3D atomic substructures with a known biological or chemical function.
• Another object of the invention is a process to identify similar 3D atomic substructures A and B according to the invention, further comprising calculation of average orientation of said 3D atomic substructures A and B with respect to the orientation of their individual atoms and a visual representation of said A and B 3D atomic substructures, wherein said visual representation is operated by means of graphs projections matching the following conditions: a) the average orientation of said 3D A atomic structure is orthogonal to the projection plane; b ) said substructure B is optimally superimposed to said substructure A.
• Figures 1 to 7 illustrate major steps of the process of comparison of 3D structures according to the invention.
• FIG. 1 shows the local density computation around a given atom A with a plot representing the weight function.
• Figure 3 represents the different vectors useful to represent geometric information associated to each chemical group and related to their spatial orientation.
• FIG. 4 illustrates the reduction of atoms to chemical groups (A) , followed by the selection of chemical groups according to the user's will (B).
• This figure reflects the particular embodiment in which three chemical groups are selected to form triangles graphs (C), followed by a computation of parameters associated to each triangle (D).
• Letters are those used in the text: , , P 2 and P 3 represent the position of the chemical groups, C l r C 2 and C 3 represent the local centers of mass associated to the chemical groups, P and C are the centers of these points .
• FIG. 5 corresponds to an input of graphs from a database (A) and illustrates the main comparison step (B) .
• FIG. 7 illustrates the process of obtaining families of substructures from an arbitrary number of structures, by starting using pairwise comparison results.
• the example shows 3 molecules denoted by Ml, M2 and M3 as in the text.
• Figures 9 to 11 illustrate the screening results and information about the legume lectin family .
• Figures 8 and 9 illustrate the comparison of serine proteases : subtili s in , 1SBC structure vs . chymotrypsin, 1AFQ structure .
• Figure 8 is a schematic view of the sequences of both functional forms of the serine proteases with highlighting of the catalytic triad residues.
• - Figure 10 illustrates results obtained by the process of comparison of the invention ; no incorrect patch was returned.
• - Figure 11 is a view of the chemical groups defining the sugar-binding site in the peanut lectin structure 2PEL.
• FIG. 12 illustrates essential aminoacids for sugar binding in concanavalin A before and after demetallization; superposition of ⁇ -carbons with 0.9 A
• the result file is shown on figure 9 and displays one similar region that consists of the catalytic triad ( Asp32/Aspl02 , His64/His57, Ser221/Serl95) represented but four chemical groups, plus a glycine (Glyl27/Gly216) which is also known to play a role in the protease activity [18].
• the structural family of legume lectins is represented by 106 structures publicly available in the
• PDB PDB .
• Many of them are functional lectins, i.e. proteins that bind oligosaccharides non-covalently, but some of them have lost the capability to bind sugar at this site in spite of their overall sequential and structural similarity (see [19] for a full review on lectins).
• Proteins without native sugar-binding ability are arcelin and ⁇ -amylases inhibitors (4 structures). 7 structures are available of demetallized lectins, i.e. lectins whose site has been deprived of Ca 2+ and Zn 2+ .
• 1DQ1 and 1DQ2 are 2 structures of concanavalin A in both native and demetallized forms: though their sequences are identical and their backbone have an RMSD of 0.9 A for ⁇ - carbons, only the first form binds a sugar 3D atomic structure.
• Structure 2PEL of the peanut lectin has been used to represent a functional lectin: its site of interaction with lactose has been selected and compared to every structure within the family. More precisely, all groups that have at least one atom closer than 4 A to any atom of the ligand were selected. Thus, 10 chemical groups covering 9 aminoacids were retained (figure 11). The result of these comparisons is summarized on figure 10. Among all structures, 91 proteins showed at least one similar patch. All of these patches were sugar binding sites from functional proteins. No patch was detected among the 11 proteins missing the sugar-binding function. Thus, only 4 functional sites were not detected, and no false positive was obtained. Local conformational changes at the binding site explain the lost of activity in the case of demetallized lectins as shown on figure 12.
• the process to identify 3D substructures of the present invention excludes nonfunctional lectins by comparing them to a functional sugar binding site in spite of a high degree of similarity in sequence and in main chain architecture. This indicates that something like structural flexibility is taken into account by said process. It is sensitive enough to detect local conformation changes that are correlated with a loss of function. The process to identify 3D substructures of the present invention is also flexible enough to ignore minor changes like those depending on the presence or absence of the ligand. Only 4% of the functional lectins were not detected.
• the process of comparison of 3D atomic structures according to the invention allows the detection of 3D structural similarities in 3D atomic structures.
• the applicant shows that 3D structural similarities are correctly detected in serine proteases that are differently folded and have unrelated sequences: Dali [20] finds no similarity between these structures and it is not possible to propose a valid sequence alignment due to the inversion of catalytic residues in the sequence.
• the structural similarity that is automatically detected by the process of the invention corresponds to a common biochemical function and an identical catalytic mechanism.
• the applicant compares structures of proteins which are known to act as competitors in a biological process that is understood with more or less precision: enzymatic catalysis, affinity for a ligand, disruption or activation of biochemical pathways, immunological cross-reactivity, inhibition of cell adhesion, etc.
• Aminoacids are composed of several critical groups that may or may not be important depending on the structural context (table 1). Knowing the 3D structure of proteins allows to model proteins with chemical groups, covalent bonds and other interactions independently from the concept of aminoacid.
• a triangle may be associated to a number of parameters that ensures that a given triangle contains an amount of information that makes it much more rare than a single group represented by a point. It stands for a minimal representation of a local environment, including an oriented plan; (2) a specific biological function is rarely provided by only one or two chemical groups;
• a limit to the representation of a 3D atomic structure by a single graph of 3D located objects is the difficulty to mix well-located and numerous objects (such as hydrogen bonds) with less located but sparser objects (such as clusters of 3 positively charged aminoacids).
• the burial of atoms is not estimated using an accessible surface area (ASA) calculation, but a notion of local atomic density.
• ASA accessible surface area
• the ASA would correspond to the emerged part of a floating body and be null for any object under the surface, whereas the density calculation is a measure of the depth of any object, even non-floating ones.
• Figure 12 shows that aspartate in the catalytic triad of serine proteases is almost completely buried, suggesting that crucial residues may be essential for protein function, and this even if they lie below the surface of the 3D atomic structure.
• This kind of depth estimation is also essential for providing a vector that is roughly orthogonal to the molecular surface: these vectors are used to estimate the angle formed between a given triplet of chemical groups and the surface.
• the process may be used to perform a large number of comparisons, especially when one of the compared elements is a small site, the following useful strategies may be used:
• Bairoch, A., PROSITE a dictionary of sites and patterns in proteins . Nucleic Acids Res, 1991. 19 Suppl : p. 2241-5.

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• Crystallography & Structural Chemistry (AREA)
• Management, Administration, Business Operations System, And Electronic Commerce (AREA)
• Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
• Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

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• 2002
  • 2002-06-06 EP EP20020291407 patent/EP1369807A1/de not_active Withdrawn
• 2003
  • 2003-06-05 CA CA 2488411 patent/CA2488411A1/en not_active Abandoned
  • 2003-06-05 WO PCT/IB2003/002928 patent/WO2003104388A2/en not_active Ceased
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