key: cord-0878058-nslxkboq
authors: Nitulescu, Georgiana; Nitulescu, George Mihai; Zanfirescu, Anca; Mihai, Dragos Paul; Gradinaru, Daniela
title: Candidates for Repurposing as Anti-Virulence Agents Based on the Structural Profile Analysis of Microbial Collagenase Inhibitors
date: 2021-12-28
journal: Pharmaceutics
DOI: 10.3390/pharmaceutics14010062
sha: b0e911b27dea048f308012303dfd31fb98ddf5e5
doc_id: 878058
cord_uid: nslxkboq

The pharmacological inhibition of the bacterial collagenases (BC) enzymes is considered a promising strategy to block the virulence of the bacteria without targeting the selection mechanism leading to drug resistance. The chemical structures of the Clostridium perfringens collagenase A (ColA) inhibitors were analyzed using Bemis-Murcko skeletons, Murcko frameworks, the type of plain rings, and docking studies. The inhibitors were classified based on their structural architecture and various scoring methods were implemented to predict the probability of new compounds to inhibit ColA and other BC. The analyses indicated that all compounds contain at least one aromatic ring, which is often a nitrobenzene fragment. 2-Nitrobenzene based compounds are, on average, more potent BC inhibitors compared to those derived from 4-nitrobenzene. The molecular descriptors MDEO-11, AATS0s, ASP-0, and MAXDN were determined as filters to identify new BC inhibitors and highlighted the necessity for a compound to contain at least three primary oxygen atoms. The DrugBank database was virtually screened using the developed methods. A total of 100 compounds were identified as potential BC inhibitors, of which, 10 are human approved drugs. Benzthiazide, entacapone, and lodoxamide were chosen as the best candidates for in vitro testing based on their pharmaco-toxicological profile.

an alternative approach that focuses on interfering with bacterial virulence factors (e.g., exotoxins, biofilm, secreted enzymes), thus interrupting the infectious process rather than bacterial growth [4] [5] [6] [7] .

Various research groups reported the existence of a promising number of anti-virulence strategies: inhibition of pore-forming toxins that kill host cells, thereby combatting immune responses and liberating nutrients from the host [8] ; inhibition of microbial adhesion and colonization (e.g., inhibition of pili formation by binding the PapD protein, a conserved chaperone for pilus assembly in uropathogenic E. coli) [9] ; inhibition of the secretion of cell surface proteins (e.g., sortases) [7] ; inhibition of regulatory bacterial function (e.g., targeting quorum sensing, a complex regulatory network that governs the expression of a series of bacterial virulence factors in response to cell density or environment changes) [10] ; inhibition of the bacterial cell wall resistance to the innate immunity (e.g., inhibition of heptose biosynthesis, heptose being the structural element within the conserved lipopolysaccharide core-the major component of the outer membrane of Gram-negative bacteria and a major determinant of their pathogenicity) [11] .

Drugs targeting anti-virulence factors are intended to be used for the treatment of bacterial infections with multi-resistant pathogens, as monotherapy or associated with classical antibacterial substances. They are also expected to possess several advantages over the latter, such as reduced selection pressure and minimal perturbation of the healthy microbiota. Some have already been approved by the US Food and Drug Administration and/or the European Medicines Agency for bacterial toxin-mediated diseases (e.g., raxibacumab, obiltoxaximab), while others targeting antibiotic-resistant bacteria have entered clinical trials (e.g., AR-301, MEDI4893) [12] .

As mentioned above, inhibition of bacterial adhesion and colonization represents a promising anti-virulence strategy. We focus on bacterial collagenases (BC) as important virulence factors involved in the growth and proliferation of pathogenic bacteria on their hosts. These proteolytic enzymes possess broad substrate specificity-they degrade both water insoluble and water soluble collagens in their triple helical regions at X-Gly bonds [13] . Due to their ability to degrade specific cell-membrane or extracellular-matrix components, they play key roles in host colonization. They contribute to the spread of the pathogen into the host tissues, ensure the influx of proper nutrients for survival and growth, e.g., amino acids into the bacterial cells, and facilitate toxins' diffusion [14, 15] .

Virulence of pathogenic bacteria was associated mostly with collagenases belonging to the M9 family or with bacterial collagenolytic proteases [16] , members of the U32 family in the MEROPS database [17] . The MEROPS M9 peptidase family comprise metalloproteases with a conserved zinc-binding motif, either (HEY/FTH) or (HEYT/VH), which functions as a catalytic domain [18] . They act as true bacterial collagenases, cleaving helical regions of fibrillar collagen molecules under physiological conditions [19] . They belong either to the M9A or to M9B subfamilies, based on differences in their amino acid sequence and catalytic function. Class I enzymes cannot digest collagen, class II enzymes cannot hydrolyze casein but are able to digest collagen, while class III enzymes are able to digest caseins, gelatin, and collagen [19] . Bacterial collagenolytic pathogenic bacteria and their collagenases are presented in Table 1 . Not characterized [17] Collagenases are reported to be important in the pathogenic process of other bacteria, such as Pseudomonas aeruginosa, Proteus mirabilis, B. anthracis, and Leptospira sp., although their specificity needs further investigation [32] [33] [34] [35] . Thus, targeting collagenases could provide a suitable strategy for developing new anti-virulence agents [36] [37] [38] . We focused on identifying inhibitors that could directly inactivate this specific virulence factor by using drug repurposing-taking already approved drugs and using them outside of their original designated medical indications. The computational drug repurposing strategy was shown to greatly reduce time and costs generally associated with standard drug discovery processes and to prevent any events related to unpredicted toxicity of a new active substance [39] [40] [41] [42] . Using molecular docking and analyses of the chemical scaffolds related to biological activity, we developed scoring functions as mathematical methods to predict the probability of new compounds to inhibit the bacterial collagenases, and further employed them to assess the suitability of repurposed non-antibiotic pharmacological agents as collagenase inhibitors.

In order to determine the structural profile of bacterial collagenase inhibitors, we used the Clostridium perfringens (strain 13/Type A) collagenase as a model and enzyme coded CHEMBL2802 in the database of the European Institute of Bioinformatics (EMBL-EBI). The chemical and bioinformatics data resource (ChEMBL) was investigated to identify all compounds registered as inhibitors on the CHEMBL2802 target [43] . The obtained set of molecules was made up of the chemical structures of bacterial collagenase A (ColA) inhibitors (bacterial collagenase inhibitors set-BCI) and their corresponding Ki values (M). The Ki values were converted to their negative logarithmic values (pKi). All the structures were processed by removing charges and keeping the largest fragment.

DataWarrior 5.2.1. software [44] was used to calculate the compounds' molecular weight (MW), logarithm of the partition coefficient (logP), number of hydrogen bonds donors (HBD), and number of hydrogen bonds acceptors (HBD). The intervals of distribution of these parameters were used to search the ChEMBL database. The resulting compounds were randomly chosen to prepare a decoy set (DCY) with 20 folds more entries than the BCI set. BCI and DCY sets were united into a single dataset, named ALL set.

The structures from the drug repository DrugBank 5.1.18 [45] were downloaded to search for new potential bacterial collagenase inhibitors. The inorganic and organometallic structures and the mixtures of compounds were removed from this set. The filters 250 ↔ 750 (MW), 7 ↔ 15 (HBA), 0 ↔ 6 (HBD), and −2.5 ↔ 6 (logP) were applied, resulting in a final database named DB.

An array of 1D and 2D molecular descriptors were calculated for all the structures in the ALL set using the freely available PaDEL-Descriptor v2.21 software [46] , using the SMILES codes extracted from the ChEMBL as input. The zero variant variables were removed. The cutoff values for the relevant descriptors were established by using a receiver operating characteristic (ROC) curve analysis. The latter was performed using IBM SPSS Statistics v20.0 software (IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY, USA: IBM Corp.). A flag variable (BC) was introduced to indicate if the value of the target descriptors is above the defined cutoff value and to indicate a potential BCI compound.

Murcko frameworks (MF) and Bemis-Murcko (BM) skeletons, representing the structural molecular frameworks incorporating only the rings and their interconnecting chains, [47] were generated for both BCI and DCY sets by using DataWarrior. Two arrays of performance scores, noted as P-MF and P-BM, were calculated as the average value of the pKi values registered for all the compounds in set ALL sharing the sub-structure of interest (Equations (1) and (2)). The values of these scores are correlated with the odds for each sub-structure to generate active compounds towards the pharmaceutical target [48] . 

DataWarrior 5.2.1 software was used to generate all ring systems existing in each compound from the ALL set. Each structure was divided in multiple fragments based on each cycle structure. The single bonded substituents were erased, while the double bonded heteroatoms connected directly to the ring system were kept. The P-PR performance score was calculated using Equation (3)-as the average value of the pKi values of all the compounds in set ALL sharing the respective plain ring (PR).

The odds variable (OD) was calculated for each PR fragment as the ratio of a PR fragment frequency in the BCI set compared to the DCY set (Equation (4)) and represents the likelihood of the scaffold to be associated with a BC inhibitor.

The structures of the compounds in the DB set were transformed in BM and PR fragments. The S-PR score was defined as the average of the P-PR values of all PR fragments found in a compound and was used to estimate the probability of that compound to inhibit BC. A total performance score (TS) was used to take into account both the S-PR score and the P-BM values, as shown in Equation (5).

The repurposing candidates that exhibited high performance scores were thereafter screened against the ColA structure using molecular docking simulations.

Since there is no readily available crystal structure of C. perfringens collagenase A, we used homology modeling methods to build tridimensional models of the bacterial protease. The sequence of the target protein was retrieved from the UniProt database (code P43153) in FASTA format. SWISS-MODEL [49] web-server and YASARA Structure [50] software are both fully automated resources that were used to search for templates and to build the protein models. The resulting models were chosen for further studies considering the sequence similarity, coverage, presence of Zn 2+ cofactor in the active site, and quality parameters. The quality assessment of generated models was performed with a SAVES v6.0 server, using ERRAT, VERIFY3D, PROVE, and PROCHECK programs [51] [52] [53] [54] .

The top ranked models were further validated by docking three highly potent ColA inhibitors into the active site using an induced fit approach, treating several key residues (e.g., Glu503, Glu534, Trp518, Tyr577, Tyr583) as flexible. The model that generated the most optimal conformations of the protein-ligand complexes was used for the virtual screening of the repurposing candidates.

A molecular docking experiment was employed for the virtual screening of the repurposable candidates to select hit molecules with potential ColA inhibitory activity. The docking studies were performed using the AutoDock Vina v1.1.2 [55] algorithm built within the YASARA Structure software. The docking grid box included only the active site within the peptidase domain, which is formed between two half-domains (central helix and gluzincin helix). The active site includes the catalytic Zn 2+ , which is complexed by two histidines (His502 and His506) and two histidine-stabilizing glutamates (Glu503 and Glu534).

The predicted protein structure that yielded the best results after induced fit docking of the selected strong inhibitors was used for further screening. The modelled protein and docking protocol were further validated by docking the BCI set into the active site and testing the correlation between experimental and predicted parameters that describe ligand efficiency.

Both protein structures and ligands were protonated according to the physiological pH (7.4). Three-dimensional structures of both BCI and DB sets were generated and energetically minimized with OpenBabel v2.4.1 software [56] , using the GAFF force field (general AMBER force field) and 1500 steps with the steepest descent algorithm. The screening algorithm used rigid side chains and performed 12 docking runs for each ligand and the results were retrieved as the binding energy (∆G, kcal/mol), ligand efficiency (∆G

o. of heavy atoms), and dissociation constant (Kd, pM) of the best binding pose. The binding poses of chosen hit repurposable candidates were rescored using the AutoDock Vina local search algorithm and energy minimization with a NOVA forcefield. The analysis of predicted conformations of the protein-ligand complexes and molecular interactions was performed using the BIOVIA Discovery Studio Visualizer (BIOVIA, Discovery Studio Visualizer, Version 17.2.0, Dassault Systèmes, 2016, San Diego, CA, USA).

A dataset of 253 structures belonging to Clostridium perfringens collagenase inhibitors, was collected from the ChEMBL database together with their corresponding inhibitor constants (Ki, M). The dataset (BCI) was filtered by removing the imprecise biological values resulting in a final dataset that contains 239 compounds.

The compounds of the BCI set had MW values in the 302.3 ↔ 731.4 g/mol range, HBA between 8 and 14, HBD between 1 and 5, and the logP values situated in the −1.99 ↔ 5.58 interval. Based on these values, the limit points for establishing the decoy set (DCY) were: 250 ↔ 750 (MW), 7 ↔ 15 (HBA), 0 ↔ 6 (HBD), and −2.5 ↔ 6 (logP). The resulting 670,703 structures from ChEMBL were randomly selected to set up the DCY set. The DCY set consists of 4780 compounds, 20-fold more than the BCI set. Thus, the ALL set consists of 5019 structures).

From the 11,172 structures downloaded from DrugBank, after screening by employing the above-mentioned filters, 2685 structures were included in the final database.

The Murcko framework (MF) consist of all the ring systems of each compound's structure and the atoms uniting them. In a relationship with the original molecule, the MF fragments don't contain any single bonded side-chain atoms. The BCI set compounds generated a number of 32 sub-structures (MF01-MF32), while the compounds in the DCY set yielded 4421 sub-structures. The null MF was produced only by compounds from the DCY set. The Shannon diversity index was calculated as 2.394 for the BCI set and 8.320 for the DCY set. This significant difference indicates that the nature of MF is important for the design of BC inhibitors.

A number of 27 MF are strictly specific to the BCI set, while MF01, MF03, MF04, and MF10 are found almost exclusively in the inactive compounds set. The P-MF values ranged between 1.91 and 8.10. The structures and P-MF values for the most frequent frameworks observed in the BCI set are presented in Figure 1 . With the exception of MF02 (benzene ring), all MF sub-structures found in the set BCI contain at least two rings and have a P-MF value over four. As an MF sub-structure, the benzene ring is associated with a low potency of BC inhibition demonstrated by the P-MF value of 1.91.

The dimension of the side-chain elements attached to the MF core was quantified as the parameter SC. It was calculated by subtracting the MW of the MF sub-structure from the MW of the whole molecule. For the BCI set, the SC value ranged between 14.03 and 643.23 with an average of 170.01, while in the DCY set the distribution was from 0 (no side-chain elements) to 598.76 with an average of 115.6. The difference is statistically significant, indicating the importance of the presence of side-chain elements. The number of heavy atoms is also statistically different: in the BCI set, the average is 30.0 atoms, while in the DCY set, the average is 32.5.

Bemis-Murcko (BM) skeletons are derived from the molecules' MF sub-structure by the removal of side-chain elements and of atom labels, and the changing of all bond types to single bonds. The 239 structures in the BCI set generated 21 distinct BM skeletons (BM01-BM21), while the 4789 compounds of the DCY set yielded 3068 BM structures. The null skeleton resulted from 0.46% of compounds in the set (22 structures). The Shannon diversity index values were calculated as 0.896 for the BCI set and 3.300 for the DCY set.

The obtained P-BM values were in the range of 0.87-7.48 and seemed to increase with the number of non-H atoms in each BM scaffold. The four major architectural types of BM skeletons resulted from the BCI set, their structures, and the corresponding P-BM values are presented in Figure 2 . The MF frameworks can be classified in four major types based on their structure. The type I contains only one ring and is represented by the benzene (MF01) structure. The architectural type II is represented by two cyclic structures linked by a linear bridge of 4 ↔ 6 atoms. Type III is derived from type II, having an additional hexagonal ring (C3) attached to the linear linker. The skeletons in the type IV category are cyclic homologues of the type III template with 2 pentagonal rings inserted in a cycle of 15 up to 21 atoms. A hexagonal ring is bound by a one atom branch to the main linker, in a similar manner to the type III skeletons.

With the exception of MF02 (benzene ring), all MF sub-structures found in the set BCI contain at least two rings and have a P-MF value over four. As an MF sub-structure, the benzene ring is associated with a low potency of BC inhibition demonstrated by the P-MF value of 1.91.

The dimension of the side-chain elements attached to the MF core was quantified as the parameter SC. It was calculated by subtracting the MW of the MF sub-structure from the MW of the whole molecule. For the BCI set, the SC value ranged between 14.03 and 643.23 with an average of 170.01, while in the DCY set the distribution was from 0 (no side-chain elements) to 598.76 with an average of 115.6. The difference is statistically significant, indicating the importance of the presence of side-chain elements. The number of heavy atoms is also statistically different: in the BCI set, the average is 30.0 atoms, while in the DCY set, the average is 32.5. The type I of BM skeletons contains only the hexagonal ring of the BM3 structure and originates from the MF01 framework. The architectural type II is represented by 7 BM structures with P-MF values in the range of 0.87-5.75. Type III is derived from type II, while the BM skeletons in the type IV category are cyclic homologues of type III.

The transformation of the 5019 structures from the ALL set in PR generated 1312 individual sub-structures, while the compounds from the BCI set generated only 17 PR fragments. All the ColA inhibitors from the BCI set contain at least one benzene ring (PR01). The second most frequent ring was the 1,3,4-thiadiazole-2-thione (PR02) with a frequency of 11.30%, followed by naphthalene (PR03) with a frequency of 7.95%, and thiophene (PR04) and quinolone (PR05), both found in 2.09% of the compounds from set BCI ( Table  2 ). The pyrrolidine ring (PR06) was found in two BCI structures. The structure PR07-PR17 is found only in the BCI set compounds, and not in the DCY set. The PR02 fragment registered the best P-PR value and the highest OD value, indicating its usefulness in designing new BC inhibitors. The naphthalene (PR03) and the benzene (PR01) presented OD values above one and fairly good P-PR values. Thiophene, The type I of BM skeletons contains only the hexagonal ring of the BM3 structure and originates from the MF01 framework. The architectural type II is represented by 7 BM structures with P-MF values in the range of 0.87-5.75. Type III is derived from type II, while the BM skeletons in the type IV category are cyclic homologues of type III.

The transformation of the 5019 structures from the ALL set in PR generated 1312 individual sub-structures, while the compounds from the BCI set generated only 17 PR fragments. All the ColA inhibitors from the BCI set contain at least one benzene ring (PR01). The second most frequent ring was the 1,3,4-thiadiazole-2-thione (PR02) with a frequency of 11.30%, followed by naphthalene (PR03) with a frequency of 7.95%, and thiophene (PR04) and quinolone (PR05), both found in 2.09% of the compounds from set BCI ( Table 2 ). The pyrrolidine ring (PR06) was found in two BCI structures. The structure PR07-PR17 is found only in the BCI set compounds, and not in the DCY set. The type I of BM skeletons contains only the hexagonal ring of the BM3 structure and originates from the MF01 framework. The architectural type II is represented by 7 BM structures with P-MF values in the range of 0.87-5.75. Type III is derived from type II, while the BM skeletons in the type IV category are cyclic homologues of type III.

The transformation of the 5019 structures from the ALL set in PR generated 1312 individual sub-structures, while the compounds from the BCI set generated only 17 PR fragments. All the ColA inhibitors from the BCI set contain at least one benzene ring (PR01). The second most frequent ring was the 1,3,4-thiadiazole-2-thione (PR02) with a frequency of 11.30%, followed by naphthalene (PR03) with a frequency of 7.95%, and thiophene (PR04) and quinolone (PR05), both found in 2.09% of the compounds from set BCI ( Table  2 ). The pyrrolidine ring (PR06) was found in two BCI structures. The structure PR07-PR17 is found only in the BCI set compounds, and not in the DCY set. The PR02 fragment registered the best P-PR value and the highest OD value, indicating its usefulness in designing new BC inhibitors. The naphthalene (PR03) and the benzene (PR01) presented OD values above one and fairly good P-PR values. Thiophene, The type I of BM skeletons contains only the hexagonal ring of the BM3 structure and originates from the MF01 framework. The architectural type II is represented by 7 BM structures with P-MF values in the range of 0.87-5.75. Type III is derived from type II, while the BM skeletons in the type IV category are cyclic homologues of type III.

The transformation of the 5019 structures from the ALL set in PR generated 1312 individual sub-structures, while the compounds from the BCI set generated only 17 PR fragments. All the ColA inhibitors from the BCI set contain at least one benzene ring (PR01). The second most frequent ring was the 1,3,4-thiadiazole-2-thione (PR02) with a frequency of 11.30%, followed by naphthalene (PR03) with a frequency of 7.95%, and thiophene (PR04) and quinolone (PR05), both found in 2.09% of the compounds from set BCI ( Table  2 ). The pyrrolidine ring (PR06) was found in two BCI structures. The structure PR07-PR17 is found only in the BCI set compounds, and not in the DCY set. The PR02 fragment registered the best P-PR value and the highest OD value, indicating its usefulness in designing new BC inhibitors. The naphthalene (PR03) and the benzene (PR01) presented OD values above one and fairly good P-PR values. Thiophene, The type I of BM skeletons contains only the hexagonal ring of the BM3 structure and originates from the MF01 framework. The architectural type II is represented by 7 BM structures with P-MF values in the range of 0.87-5.75. Type III is derived from type II, while the BM skeletons in the type IV category are cyclic homologues of type III.

The transformation of the 5019 structures from the ALL set in PR generated 1312 individual sub-structures, while the compounds from the BCI set generated only 17 PR fragments. All the ColA inhibitors from the BCI set contain at least one benzene ring (PR01). The second most frequent ring was the 1,3,4-thiadiazole-2-thione (PR02) with a frequency of 11.30%, followed by naphthalene (PR03) with a frequency of 7.95%, and thiophene (PR04) and quinolone (PR05), both found in 2.09% of the compounds from set BCI ( Table  2 ). The pyrrolidine ring (PR06) was found in two BCI structures. The structure PR07-PR17 is found only in the BCI set compounds, and not in the DCY set. The PR02 fragment registered the best P-PR value and the highest OD value, indicating its usefulness in designing new BC inhibitors. The naphthalene (PR03) and the benzene (PR01) presented OD values above one and fairly good P-PR values. Thiophene, The type I of BM skeletons contains only the hexagonal ring of the BM3 structure and originates from the MF01 framework. The architectural type II is represented by 7 BM structures with P-MF values in the range of 0.87-5.75. Type III is derived from type II, while the BM skeletons in the type IV category are cyclic homologues of type III.

The transformation of the 5019 structures from the ALL set in PR generated 1312 individual sub-structures, while the compounds from the BCI set generated only 17 PR fragments. All the ColA inhibitors from the BCI set contain at least one benzene ring (PR01). The second most frequent ring was the 1,3,4-thiadiazole-2-thione (PR02) with a frequency of 11.30%, followed by naphthalene (PR03) with a frequency of 7.95%, and thiophene (PR04) and quinolone (PR05), both found in 2.09% of the compounds from set BCI ( Table  2 ). The pyrrolidine ring (PR06) was found in two BCI structures. The structure PR07-PR17 is found only in the BCI set compounds, and not in the DCY set. The PR02 fragment registered the best P-PR value and the highest OD value, indicating its usefulness in designing new BC inhibitors. The naphthalene (PR03) and the benzene (PR01) presented OD values above one and fairly good P-PR values. Thiophene, The type I of BM skeletons contains only the hexagonal ring of the BM3 structure and originates from the MF01 framework. The architectural type II is represented by 7 BM structures with P-MF values in the range of 0.87-5.75. Type III is derived from type II, while the BM skeletons in the type IV category are cyclic homologues of type III.

The transformation of the 5019 structures from the ALL set in PR generated 1312 individual sub-structures, while the compounds from the BCI set generated only 17 PR fragments. All the ColA inhibitors from the BCI set contain at least one benzene ring (PR01). The second most frequent ring was the 1,3,4-thiadiazole-2-thione (PR02) with a frequency of 11.30%, followed by naphthalene (PR03) with a frequency of 7.95%, and thiophene (PR04) and quinolone (PR05), both found in 2.09% of the compounds from set BCI ( Table  2 ). The pyrrolidine ring (PR06) was found in two BCI structures. The structure PR07-PR17 is found only in the BCI set compounds, and not in the DCY set. The PR02 fragment registered the best P-PR value and the highest OD value, indicating its usefulness in designing new BC inhibitors. The naphthalene (PR03) and the benzene (PR01) presented OD values above one and fairly good P-PR values. Thiophene, The type I of BM skeletons contains only the hexagonal ring of the BM3 structure and originates from the MF01 framework. The architectural type II is represented by 7 BM structures with P-MF values in the range of 0.87-5.75. Type III is derived from type II, while the BM skeletons in the type IV category are cyclic homologues of type III.

The transformation of the 5019 structures from the ALL set in PR generated 1312 individual sub-structures, while the compounds from the BCI set generated only 17 PR fragments. All the ColA inhibitors from the BCI set contain at least one benzene ring (PR01). The second most frequent ring was the 1,3,4-thiadiazole-2-thione (PR02) with a frequency of 11.30%, followed by naphthalene (PR03) with a frequency of 7.95%, and thiophene (PR04) and quinolone (PR05), both found in 2.09% of the compounds from set BCI ( Table  2 ). The pyrrolidine ring (PR06) was found in two BCI structures. The structure PR07-PR17 is found only in the BCI set compounds, and not in the DCY set. The PR02 fragment registered the best P-PR value and the highest OD value, indicating its usefulness in designing new BC inhibitors. The naphthalene (PR03) and the Pharmaceutics 2022, 14, 62 9 of 21 benzene (PR01) presented OD values above one and fairly good P-PR values. Thiophene, quinolone, and pyrrolidine had OD values below one, and therefore a low predictable value in identifying new BC inhibitors.

The PR analysis coupled with the BM architecture of the compounds from the BCI set indicated a high chemical similarity and highlighted the importance of some cyclic elements presented in Figure 3 . The value I represents the average of the pKi values for each sub-structure across the BCI set.

Pharmaceutics 2022, 14, x FOR PEER REVIEW 9 of 21 quinolone, and pyrrolidine had OD values below one, and therefore a low predictable value in identifying new BC inhibitors. The PR analysis coupled with the BM architecture of the compounds from the BCI set indicated a high chemical similarity and highlighted the importance of some cyclic elements presented in Figure 3 . The value I represents the average of the pKi values for each sub-structure across the BCI set. The BCI compounds from types I and II contain ring A exclusively, a 2-nitrobenzene (A1) or a 4-nitrobenzene (A2) fragment. The value of I indicates a significantly higher potency for the 2-nitro substituted compounds compared to the 4-nitro derivatives. In types III and IV, the ring A is represented by a benzene (A3) with no other substituent. In the structure of types II and III inhibitors, the ring A is bound by a linker to the ring B, which is very similar in both types. The ring B1 represents a benzene with various substitution patterns, while rings B2, B4, and B5 are not substituted. The score I is significantly higher for the rings B2-B5 found in type II inhibitors, compared to the type III structures, indicating the importance of the overall geometry.

Most of the type III compounds have, as a C ring, the 1,3,4-thiadiazole-2-thione (PR-02), while only 2 compounds contain a 2-substituted pyrrolidine ring (PR06). The 12 compounds sharing the type IV structural architecture have the same ring elements A3, B7, and C3, with the 2 pyrrolidine cycles bounded in a large cyclic structure that has branched a benzyl fragment. The differences in the potency of these compounds comes from the dimension and the elements of the cyclic structure linking rings B and C.

All the structures form the ALL set were introduced in the PaDEL software and a number of 1444 1D and 2D descriptors were computed. After removing those with 0 variance, 1192 descriptors remained. A ROC analysis was executed for them, returning 8 descriptors with values of the area under the curve (AUC) parameter over 0.9. A cutoff value was established for each descriptor, considering 100% sensitivity and the highest possible specificity (Table 3) . The BCI compounds from types I and II contain ring A exclusively, a 2-nitrobenzene (A1) or a 4-nitrobenzene (A2) fragment. The value of I indicates a significantly higher potency for the 2-nitro substituted compounds compared to the 4-nitro derivatives. In types III and IV, the ring A is represented by a benzene (A3) with no other substituent. In the structure of types II and III inhibitors, the ring A is bound by a linker to the ring B, which is very similar in both types. The ring B1 represents a benzene with various substitution patterns, while rings B2, B4, and B5 are not substituted. The score I is significantly higher for the rings B2-B5 found in type II inhibitors, compared to the type III structures, indicating the importance of the overall geometry.

Most of the type III compounds have, as a C ring, the 1,3,4-thiadiazole-2-thione (PR-02), while only 2 compounds contain a 2-substituted pyrrolidine ring (PR06). The 12 compounds sharing the type IV structural architecture have the same ring elements A3, B7, and C3, with the 2 pyrrolidine cycles bounded in a large cyclic structure that has branched a benzyl fragment. The differences in the potency of these compounds comes from the dimension and the elements of the cyclic structure linking rings B and C.

All the structures form the ALL set were introduced in the PaDEL software and a number of 1444 1D and 2D descriptors were computed. After removing those with 0 variance, 1192 descriptors remained. A ROC analysis was executed for them, returning 8 descriptors with values of the area under the curve (AUC) parameter over 0.9. A cutoff value was established for each descriptor, considering 100% sensitivity and the highest possible specificity (Table 3) . (7) where, when the BC variable takes the value 1, it indicates a BCI compound. This method of identification has a sensitivity of 100% and a specificity of 89.58%.

The MDEO-11 descriptor is based on the molecular graph and accounts only for oxygen atoms bonded to only one non-hydrogen atom, irrespective of bond types (primary oxygen atoms) [57] . The MDEO-11 value represents the number of primary oxygen atoms divided by the geometrical average of the graph distances between each pair of primary oxygen atoms. In the BCI set, this type of oxygen atom can be found in groups like: -OH, >C=O, -NO 2 , and -SO 2 -. For a compound to have a value above the cutoff of 1.194, it needs at least 3 such oxygen atoms. The distance between these atoms is also very important, as the descriptor value decreases as the distance increases. Considering type I inhibitors, they need at least an oxygen atom in the chain close to the nitro group.

The ASP-0 descriptor represents the SP-0 descriptor divided by the number of atoms in the compound's structure. SP-0 is based on the molecular graph and takes account of the number of edges for each node. It is calculated as a sum of the reciprocal square root for each number of edges in a node. In the class of n-alkanes, the highest possible value of ASP-0 is 1, found in the case of methane and ethane, and decreases as the carbon chain is longer. The branched derivatives have higher ASP-0 values compared to their unbranched isomers.

Both AATS0s and MAXDN use the Kier-Hall intrinsic state values (Is). AATS0s is calculated as the sum of each atom squared Is value, divided then by the number of all atoms. For a compound to have an AATS0s value above 5.239, it needs atoms like fluorine, chlorine, oxygen, nitrogen, or multiple bonded carbon atoms. The MAXDN represents the maximum negative value registered in a compound for the sum of the ratio of differences of Is values and the corresponding topological distances. It is considered a measure of the molecule's nucleophilicity [58] .

A molecular docking study was performed for ColA inhibitors to validate a virtual screening protocol for repurposing DB candidates. The 3D structure of C. perfringens ColA has not been solved yet; therefore, several models of the protein were built using two structure prediction tools. The first four templates identified by SWISS-MODEL and YASARA ranked by sequence coverage were used for model generation. The quality parameters of the built models are shown in Table 4 . Both tools found crystal structures 2Y3U, 4ARE, and 4AR9 as potential templates, while 5O7E was identified only by SWISS-MODEL, and 5IKU by YASARA, respectively. The best overall quality factor calculated with ERRAT was found for the model built using 2Y3U with YASARA. However, this structure lacks the catalytic Zn 2+ and could not be used for further studies. On the other hand, the worst overall quality factor was calculated for the model generated using 5IKU structure (76.9874), which has only 21% coverage of the ColA sequence and shares only 35.37% sequence identity. The structure built using the 5IKU template corresponds to the ColA collagen-binding domain and is not suitable for the aim of this study. The models generated using 2Y3U and 4ARE (69 and 60% coverage, 49.27 and 49.78% sequence identity) crystal structures included the metalloprotease S1 domain, consisting of an activator domain, a catalytic subdomain, and a helper subdomain. Models built using 4AR9 and 5O7E included only the catalytic and helper subdomains (35% coverage, 55.55%, and 58.44% sequence identity), which were sufficient for the molecular docking simulations. Three potent ColA inhibitors from different chemical classes were docked into the catalytic site using an induced fit approach to select the most suitable target protein for further screening. The molecular docking experiment was performed for models Y1, Y3, S4, and Y5 using an oxime derivative (CHEMBL306726, pKi = 8.3 M), a 1,3,4-thiadiazole-2thione derivative (CHEMBL3142607, pKi = 6.7), and a peptide analogue inhibitor with a phosphonyl group (CHEMBL2115242, pKi = 8.1). Only docking with model S4 (Figure 4 ) yielded satisfying results regarding the binding poses of all three inhibitors.

This model had a 95.0954 overall quality factor. In general, proteins with high resolution structures have overall quality factors around 95 or higher. The Z-score of the model was 0.291. Moreover, the analysis of the Ramachandran plot showed that the phi-psi torsion angle for 92.5% of residues are in the most favored regions, 23 residues (6.9%) are in additional allowed regions, 2 residues (Gln433 and Glu440) are in generously allowed regions, while 0 residues fall into disallowed regions ( Figure 5 ). Pharmaceutics 2022, 14, x FOR PEER REVIEW 12 of 21 This model had a 95.0954 overall quality factor. In general, proteins with high resolution structures have overall quality factors around 95 or higher. The Z-score of the model was 0.291. Moreover, the analysis of the Ramachandran plot showed that the phipsi torsion angle for 92.5% of residues are in the most favored regions, 23 residues (6.9%) are in additional allowed regions, 2 residues (Gln433 and Glu440) are in generously allowed regions, while 0 residues fall into disallowed regions ( Figure 5 ). This model had a 95.0954 overall quality factor. In general, proteins with high resolution structures have overall quality factors around 95 or higher. The Z-score of the model was 0.291. Moreover, the analysis of the Ramachandran plot showed that the phipsi torsion angle for 92.5% of residues are in the most favored regions, 23 residues (6.9%) are in additional allowed regions, 2 residues (Gln433 and Glu440) are in generously allowed regions, while 0 residues fall into disallowed regions ( Figure 5 ). The binding poses of the three inhibitors in the active site are shown in Figure 6A . All three inhibitors interact with the catalytic zinc. The docking protocol was further validated by docking the BCI set and calculating the determination coefficient between experimental and predicted values of ligand efficiencies. Good correlations were obtained between experimental and predicted surface-binding efficiency index values (SEI), after splitting the BCI set into strong (pKi > 7, R 2 = 0.8281) and weak (pKi < 7, R 2 = 0.6724) inhibitors ( Figure 6B,C) . SEI is calculated as the activity value divided by polar surface area/100 Å).

between experimental and predicted surface-binding efficiency index values (SEI), after splitting the BCI set into strong (pKi > 7, R 2 = 0.8281) and weak (pKi < 7, R 2 = 0.6724) inhibitors ( Figure 6B,C) . SEI is calculated as the activity value divided by polar surface area/100 Å).

The docking scores (ΔG) of the compounds from the BCI set ranged from −11.14 to −6.23 kcal/mol with a mean value of −8.35 kcal/mol. Forty-seven amino acids are involved in overall interactions with the compounds from the BCI set, the most frequent being Asn471 (100%), Tyr583 (95.78%), Gly473 (75.53%), Glu534 (68.78%), and Ile474 (67.93%). On average a compound from the BCI set interacts with of 15.63 amino acid residues (values ranging between 11 and 26). The docking scores (∆G) of the compounds from the BCI set ranged from −11.14 to −6.23 kcal/mol with a mean value of −8.35 kcal/mol. Forty-seven amino acids are involved in overall interactions with the compounds from the BCI set, the most frequent being Asn471 (100%), Tyr583 (95.78%), Gly473 (75.53%), Glu534 (68.78%), and Ile474 (67.93%). On average a compound from the BCI set interacts with of 15.63 amino acid residues (values ranging between 11 and 26).

The compounds from the DB set were transformed in their corresponding BM and PR fragments. A total of 194 structures were found to generate one of the active types of BM structures (BM01-BM21). Among these molecules, 24 are classified as approved, 137 as experimental, and 40 as investigational drugs. A total number of 112 compounds share the BM03 sub-structure, belonging to type I architecture, while 82 compounds have a type II BM sub-structure.

The DB set was searched to find the compounds that generate one of the PR01-PR17 structures, thus identifying 1806 compounds. For each compound the average of the P-PR values (S-PR) was calculated to estimate the probability to inhibit BC. A number of 1806 compounds produced at least one of the target PR fragments and their calculated S-PR value ranged from 0.065 to 2.669.

The DB compounds were used to calculate the BC flag value based on the chemical descriptors computed by PaDEL software. The value 1 that indicates a potential BC inhibitor was obtained for 664 compounds.

The sum of S-PR and P-BM values were added to obtain the TS score to select the DB compounds with the best chances to inhibit ColA. Of all the 2685 compounds of the DB set, only 194 presented TS values over 1, and 100 of them also had BC values equal to 1. These compounds were chosen as candidates for molecular docking screening. The compounds with the best 10 TS values are presented in Table 5 along with their main target of action as registered in DrugBank. 

The compounds from the DB set were transformed in their corresponding BM and PR fragments. A total of 194 structures were found to generate one of the active types of BM structures (BM01-BM21). Among these molecules, 24 are classified as approved, 137 as experimental, and 40 as investigational drugs. A total number of 112 compounds share the BM03 sub-structure, belonging to type I architecture, while 82 compounds have a type II BM sub-structure.

The DB set was searched to find the compounds that generate one of the PR01-PR17 structures, thus identifying 1806 compounds. For each compound the average of the P-PR values (S-PR) was calculated to estimate the probability to inhibit BC. A number of 1806 compounds produced at least one of the target PR fragments and their calculated S-PR value ranged from 0.065 to 2.669.

The DB compounds were used to calculate the BC flag value based on the chemical descriptors computed by PaDEL software. The value 1 that indicates a potential BC inhibitor was obtained for 664 compounds.

The sum of S-PR and P-BM values were added to obtain the TS score to select the DB compounds with the best chances to inhibit ColA. Of all the 2685 compounds of the DB set, only 194 presented TS values over 1, and 100 of them also had BC values equal to 1. These compounds were chosen as candidates for molecular docking screening. The compounds with the best 10 TS values are presented in Table 5 along with their main target of action as registered in DrugBank. 

The compounds from the DB set were transformed in their corresponding BM and PR fragments. A total of 194 structures were found to generate one of the active types of BM structures (BM01-BM21). Among these molecules, 24 are classified as approved, 137 as experimental, and 40 as investigational drugs. A total number of 112 compounds share the BM03 sub-structure, belonging to type I architecture, while 82 compounds have a type II BM sub-structure.

The DB set was searched to find the compounds that generate one of the PR01-PR17 structures, thus identifying 1806 compounds. For each compound the average of the P-PR values (S-PR) was calculated to estimate the probability to inhibit BC. A number of 1806 compounds produced at least one of the target PR fragments and their calculated S-PR value ranged from 0.065 to 2.669.

The DB compounds were used to calculate the BC flag value based on the chemical descriptors computed by PaDEL software. The value 1 that indicates a potential BC inhibitor was obtained for 664 compounds.

The sum of S-PR and P-BM values were added to obtain the TS score to select the DB compounds with the best chances to inhibit ColA. Of all the 2685 compounds of the DB set, only 194 presented TS values over 1, and 100 of them also had BC values equal to 1. These compounds were chosen as candidates for molecular docking screening. The compounds with the best 10 TS values are presented in Table 5 along with their main target of action as registered in DrugBank. 

The compounds from the DB set were transformed in their corresponding BM and PR fragments. A total of 194 structures were found to generate one of the active types of BM structures (BM01-BM21). Among these molecules, 24 are classified as approved, 137 as experimental, and 40 as investigational drugs. A total number of 112 compounds share the BM03 sub-structure, belonging to type I architecture, while 82 compounds have a type II BM sub-structure.

The DB set was searched to find the compounds that generate one of the PR01-PR17 structures, thus identifying 1806 compounds. For each compound the average of the P-PR values (S-PR) was calculated to estimate the probability to inhibit BC. A number of 1806 compounds produced at least one of the target PR fragments and their calculated S-PR value ranged from 0.065 to 2.669.

The DB compounds were used to calculate the BC flag value based on the chemical descriptors computed by PaDEL software. The value 1 that indicates a potential BC inhibitor was obtained for 664 compounds.

The sum of S-PR and P-BM values were added to obtain the TS score to select the DB compounds with the best chances to inhibit ColA. Of all the 2685 compounds of the DB set, only 194 presented TS values over 1, and 100 of them also had BC values equal to 1. These compounds were chosen as candidates for molecular docking screening. The compounds with the best 10 TS values are presented in Table 5 along with their main target of action as registered in DrugBank. The compounds from the DB set were transformed in their corresponding BM and PR fragments. A total of 194 structures were found to generate one of the active types of BM structures (BM01-BM21). Among these molecules, 24 are classified as approved, 137 as experimental, and 40 as investigational drugs. A total number of 112 compounds share the BM03 sub-structure, belonging to type I architecture, while 82 compounds have a type II BM sub-structure.

The DB set was searched to find the compounds that generate one of the PR01-PR17 structures, thus identifying 1806 compounds. For each compound the average of the P-PR values (S-PR) was calculated to estimate the probability to inhibit BC. A number of 1806 compounds produced at least one of the target PR fragments and their calculated S-PR value ranged from 0.065 to 2.669.

The DB compounds were used to calculate the BC flag value based on the chemical descriptors computed by PaDEL software. The value 1 that indicates a potential BC inhibitor was obtained for 664 compounds.

The sum of S-PR and P-BM values were added to obtain the TS score to select the DB compounds with the best chances to inhibit ColA. Of all the 2685 compounds of the DB set, only 194 presented TS values over 1, and 100 of them also had BC values equal to 1. These compounds were chosen as candidates for molecular docking screening. The compounds with the best 10 TS values are presented in Table 5 along with their main target of action as registered in DrugBank. 

• Macrophage metalloelastase • Interstitial collagenase The compound DB08498 is the chloro-derivative of DB08497, both compounds being under development as caspase-3 inhibitors. DB03124 and DB08229 are also caspase-3 inhibitors. Caspase-3 is a cysteine protease that cleaves several proteins after aspartic acid residues in specific sequences [59] . The results indicate a possible similarity between the structures of the catalytic domain of both enzymes. The compound DB07556 targets both the macrophage metalloelastase and the interstitial collagenase, metalloproteases that are similar with ColA. DB07290 inhibits the Bacillus anthracis lethal toxin, a zinc-dependent peptidase presenting the consensus sequence HEXXH [60] , similar with ColA.

The binding energy, ligand efficiency, and dissociation constant obtained from docking simulations for compounds with the best TS values are shown in Table 6 . The compound DB08498 is the chloro-derivative of DB08497, both compounds being under development as caspase-3 inhibitors. DB03124 and DB08229 are also caspase-3 inhibitors. Caspase-3 is a cysteine protease that cleaves several proteins after aspartic acid residues in specific sequences [59] . The results indicate a possible similarity between the structures of the catalytic domain of both enzymes. The compound DB07556 targets both the macrophage metalloelastase and the interstitial collagenase, metalloproteases that are similar with ColA. DB07290 inhibits the Bacillus anthracis lethal toxin, a zinc-dependent peptidase presenting the consensus sequence HEXXH [60] , similar with ColA.

The binding energy, ligand efficiency, and dissociation constant obtained from docking simulations for compounds with the best TS values are shown in Table 6 . The compound DB08498 is the chloro-derivative of DB08497, both compounds being under development as caspase-3 inhibitors. DB03124 and DB08229 are also caspase-3 inhibitors. Caspase-3 is a cysteine protease that cleaves several proteins after aspartic acid residues in specific sequences [59] . The results indicate a possible similarity between the structures of the catalytic domain of both enzymes. The compound DB07556 targets both the macrophage metalloelastase and the interstitial collagenase, metalloproteases that are similar with ColA. DB07290 inhibits the Bacillus anthracis lethal toxin, a zinc-dependent peptidase presenting the consensus sequence HEXXH [60] , similar with ColA.

The binding energy, ligand efficiency, and dissociation constant obtained from docking simulations for compounds with the best TS values are shown in Table 6 . The compound DB08498 is the chloro-derivative of DB08497, both compounds being under development as caspase-3 inhibitors. DB03124 and DB08229 are also caspase-3 inhibitors. Caspase-3 is a cysteine protease that cleaves several proteins after aspartic acid residues in specific sequences [59] . The results indicate a possible similarity between the structures of the catalytic domain of both enzymes. The compound DB07556 targets both the macrophage metalloelastase and the interstitial collagenase, metalloproteases that are similar with ColA. DB07290 inhibits the Bacillus anthracis lethal toxin, a zinc-dependent peptidase presenting the consensus sequence HEXXH [60] , similar with ColA.

The binding energy, ligand efficiency, and dissociation constant obtained from docking simulations for compounds with the best TS values are shown in Table 6 . The compound DB08498 is the chloro-derivative of DB08497, both compounds being under development as caspase-3 inhibitors. DB03124 and DB08229 are also caspase-3 inhibitors. Caspase-3 is a cysteine protease that cleaves several proteins after aspartic acid residues in specific sequences [59] . The results indicate a possible similarity between the structures of the catalytic domain of both enzymes. The compound DB07556 targets both the macrophage metalloelastase and the interstitial collagenase, metalloproteases that are similar with ColA. DB07290 inhibits the Bacillus anthracis lethal toxin, a zinc-dependent peptidase presenting the consensus sequence HEXXH [60] , similar with ColA.

The binding energy, ligand efficiency, and dissociation constant obtained from docking simulations for compounds with the best TS values are shown in Table 6 . The compound DB08498 is the chloro-derivative of DB08497, both compounds being under development as caspase-3 inhibitors. DB03124 and DB08229 are also caspase-3 inhibitors. Caspase-3 is a cysteine protease that cleaves several proteins after aspartic acid residues in specific sequences [59] . The results indicate a possible similarity between the structures of the catalytic domain of both enzymes. The compound DB07556 targets both the macrophage metalloelastase and the interstitial collagenase, metalloproteases that are similar with ColA. DB07290 inhibits the Bacillus anthracis lethal toxin, a zinc-dependent peptidase presenting the consensus sequence HEXXH [60] , similar with ColA.

The binding energy, ligand efficiency, and dissociation constant obtained from docking simulations for compounds with the best TS values are shown in Table 6 .

Of the 100 candidates, only 10 are approved for human use: benzthiazide, bortezomib, ioxitalamic acid, entacapone, technetium tc-99 m disofenin, chloramphenicol succinate, mebrofenin, lodoxamide, betiatide, and nafcillin. The ioxitalamic acid, technetium tc-99 m disofenin, mebrofenin, and betiatide are all used for diagnostic purposes and have a low druglike character because of their toxicity risks [61, 62] . Chloramphenicol succinate and nafcillin are antimicrobial drugs [63] and their effects on bacterial growth can promote resistant strains limiting their development as BCI. Benzthiazide (thiazide diuretic), entacapone (catechol-O-methyltransferase inhibitor, Parkinson's disease) and lodoxamide (mast cell stabilizer, antiallergic) emerged as the best candidates for development as antivirulence agents in infections with pathogens form the Clostridium, Pseudomonas, Vibrio, and Streptomyces genuses. The predicted binding affinities, ligand efficiencies, and the residues involved in H-bonding are presented in Table 7 . All three hit compounds showed ligand efficiency values above 0.35, which indicates good binding to the target protein. Among these molecules, benzthiazide exhibited the highest binding affinity, with −9.54 kcal/mol binding energy and 6.99 M pKd. Entacapone and lodoxamide had lower affinities and pKd values, correlated with the smaller TS score. The molecular docking simulations showed that benzthiazide forms a direct metalacceptor interaction with the catalytic zinc via the sulfone moiety within the thiazide scaffold ( Figure 7) . It forms five hydrogen bonds with Gly578, Tyr583, Asp470 and the catalytic Glu503. The protein-ligand complex is further stabilized by forming several nonpolar interactions, such as pi-sulfur, pi-pi stacked, pi-alkyl and van der Waals interactions. (Figure 7) . It forms five hydrogen bonds with Gly578, Tyr583, Asp470 and the catalytic Glu503. The protein-ligand complex is further stabilized by forming several nonpolar interactions, such as pi-sulfur, pi-pi stacked, pi-alkyl and van der Waals interactions. In the docking experiment, entacapone binds into the ColA active site via five hydrogen bonds with four residues (Tyr475, Trp518, Tyr577, Tyr583). A carbon-hydrogen bond, pi-alkyl, and pi-pi stacked interactions are also present ( Figure 8 ). Although entacapone does not interact directly with Zn 2+ , it interacts with metal-binding residues Glu533, Glu534, and His506 through van der Waals weak forces. Moreover, the substituted nitrobenzene moiety interacts with the binding pocket in a similar fashion with the docked ColA inhibitors from the BCI set ( Figure 6A ). In the docking experiment, entacapone binds into the ColA active site via five hydrogen bonds with four residues (Tyr475, Trp518, Tyr577, Tyr583). A carbon-hydrogen bond, pi-alkyl, and pi-pi stacked interactions are also present ( Figure 8 ). Although entacapone does not interact directly with Zn 2+ , it interacts with metal-binding residues Glu533, Glu534, and His506 through van der Waals weak forces. Moreover, the substituted nitrobenzene moiety interacts with the binding pocket in a similar fashion with the docked ColA inhibitors from the BCI set ( Figure 6A ). (Figure 7) . It forms five hydrogen bonds with Gly578, Tyr583, Asp470 and the catalytic Glu503. The protein-ligand complex is further stabilized by forming several nonpolar interactions, such as pi-sulfur, pi-pi stacked, pi-alkyl and van der Waals interactions. In the docking experiment, entacapone binds into the ColA active site via five hydrogen bonds with four residues (Tyr475, Trp518, Tyr577, Tyr583). A carbon-hydrogen bond, pi-alkyl, and pi-pi stacked interactions are also present ( Figure 8 ). Although entacapone does not interact directly with Zn 2+ , it interacts with metal-binding residues Glu533, Glu534, and His506 through van der Waals weak forces. Moreover, the substituted nitrobenzene moiety interacts with the binding pocket in a similar fashion with the docked ColA inhibitors from the BCI set ( Figure 6A ). The predicted binding pose of lodoxamide into the ColA active site showed an attractive interaction between the negatively charged carboxyl moiety and the catalytic zinc. Lodoxamide binds to the enzyme via six hydrogen bonds formed with five residues (Asn471, Gly472, Gly473, Tyr583 and catalytic Glu503). Moreover, several nonpolar interac-tions are formed with the binding pocket, such as van der Waals, pi-sulfur, pi-sigma, and pi-lakyl interactions (Figure 9 ). The predicted binding pose of lodoxamide into the ColA active site showed an attractive interaction between the negatively charged carboxyl moiety and the catalytic zinc. Lodoxamide binds to the enzyme via six hydrogen bonds formed with five residues (Asn471, Gly472, Gly473, Tyr583 and catalytic Glu503). Moreover, several nonpolar interactions are formed with the binding pocket, such as van der Waals, pi-sulfur, pi-sigma, and pi-lakyl interactions (Figure 9 ). 

The results of the structural analysis indicated the importance of the molecular geometry for the biological activity, as a limited array of scaffolds and cyclic structures were found to be connected with the capacity to inhibit ColA. The descriptors observed as relevant in discriminating active and inactive compounds are based also on the molecular geometry, highlighting its importance. The presence of certain types of atoms or groups of atoms seems to be essential as well. A quantitative structure-activity relationship (QSAR) study on 5-amino-2-mercapto-1,3,4-thiadiazole derivatives that inhibit the related Clostridium histolyticum collagenase indicated the importance of an amide function close to a sulfonamide group and the branching of the molecule [64] . Our research confirms these results and generalizes them using the number of primary oxygen atoms and their relative distances.

The use of scoring methods coupled with the selected molecular descriptors proved to be useful in identifying several metalloproteases inhibitors. The method was initially implemented to prioritize the compounds in the docking studies but proved to render good results on its own. It can be easily used for any database of compounds but needs their transformation to their corresponding Bemis-Murcko skeletons, plain rings, and molecular descriptors. The identified architectural types are a simple method to prepare analogs of the obtained candidates in the process of lead optimization.

A total of 100 repurposable candidates were subjected to molecular docking simulations to identify potential ColA inhibitors. The docking results revealed that benzthiazide and lodoxamide can interact directly with the Zn cofactor, while entacapone interacted with the amino acid residues within the active site. These results are similar with the pharmacological profile of benzthiazide. Contrary to other thiazide class compounds, it binds 

The results of the structural analysis indicated the importance of the molecular geometry for the biological activity, as a limited array of scaffolds and cyclic structures were found to be connected with the capacity to inhibit ColA. The descriptors observed as relevant in discriminating active and inactive compounds are based also on the molecular geometry, highlighting its importance. The presence of certain types of atoms or groups of atoms seems to be essential as well. A quantitative structure-activity relationship (QSAR) study on 5-amino-2-mercapto-1,3,4-thiadiazole derivatives that inhibit the related Clostridium histolyticum collagenase indicated the importance of an amide function close to a sulfonamide group and the branching of the molecule [64] . Our research confirms these results and generalizes them using the number of primary oxygen atoms and their relative distances.

The use of scoring methods coupled with the selected molecular descriptors proved to be useful in identifying several metalloproteases inhibitors. The method was initially implemented to prioritize the compounds in the docking studies but proved to render good results on its own. It can be easily used for any database of compounds but needs their transformation to their corresponding Bemis-Murcko skeletons, plain rings, and molecular descriptors. The identified architectural types are a simple method to prepare analogs of the obtained candidates in the process of lead optimization.

A total of 100 repurposable candidates were subjected to molecular docking simulations to identify potential ColA inhibitors. The docking results revealed that benzthiazide and lodoxamide can interact directly with the Zn cofactor, while entacapone interacted with the amino acid residues within the active site. These results are similar with the pharmacological profile of benzthiazide. Contrary to other thiazide class compounds, it binds strongly to all human carbonic anhydrases isoforms coordinating the zinc ion through the sulfonamide group [65] .

The major limitation of the study is the small number of known inhibitors and their low structural diversity. The chances of finding new lead compounds are hindered by these problems and the methods we used prioritized sensitivity over specificity as a mean to overcome this drawback. This approach may have yielded a higher number of false positives. The candidates need to be confirmed by in intro assays to be further developed as repurposed agents.

A virtual screening algorithm was implemented using both ligand-based and structurebased drug discovery approaches to identify novel potential inhibitors of C. perfringens collagenase A. Benzthiazide, entacapone, and lodoxamide are three approved drugs that we propose as potential repurposable anti-virulence agents, based on the predicted probability of inhibiting the bacterial collagenase and the favorable simulated interactions with the enzyme. Further studies are needed for the selected molecules to confirm the predicted biological activity and to assess the efficacy in treating C. perfringens infections. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: https://www.ebi.ac.uk/chembl/ and https://go.drugbank.com/. Access date: 6 September 2021.

Tackling threats and future problems of multidrug-resistant bacteria

Antibiotic Restriction Might Facilitate the Emergence of Multi-drug Resistance

Antivirulence as a new antibacterial approach for chemotherapy

The biology and future prospects of antivirulence therapies

Possible drugs for the treatment of bacterial infections in the future: Anti-virulence drugs

Inhibition of bacterial toxin recognition of membrane components as an anti-virulence strategy

Targeting bacterial sortases in search of anti-virulence therapies with low risk of resistance development

Pharmacological targeting of pore-forming toxins as adjunctive therapy for invasive bacterial infection

Design, synthesis and evaluation of peptidomimetics based on substituted bicyclic 2-pyridones-Targeting virulence of uropathogenic E. coli

Synthetic furanones inhibit quorum-sensing and enhance bacterial clearance in Pseudomonas aeruginosa lung infection in mice

Structure of the Lipopolysaccharide Core Region

Different drugs for bad bugs: Antivirulence strategies in the age of antibiotic resistance

Collagen and collagenolytic proteases: A review

Bacterial collagenases-A review

Inhibition and activity regulation of bacterial collagenases

Diversity, structures, and collagen-degrading mechanisms of bacterial collagenolytic proteases

MEROPS: The peptidase database

Structural basis for activity regulation and substrate preference of clostridial collagenases G, H, and T

Bacterial collagenases and collagen-degrading enzymes and their potential role in human disease

Identification of Clostridium histolyticum collagenase hyperreactive sites in type I, II, and III collagens: Lack of correlation with local triple helical stability

Cloning and nucleotide sequence analysis of the colH gene from Clostridium histolyticum encoding a collagenase and a gelatinase

Biochemical characterization of the catalytic domains of three different clostridial collagenases

Protein Truncating Variants of colA in Clostridium perfringens Type G Strains

Differential gene expression and extracellular secretion of the collagenolytic enzymes by the pathogen Vibrio parahaemolyticus

Handbook of Proteolytic Enzymes

Evidence for contribution of tripartite hemolysin BL, phosphatidylcholinepreferring phospholipase C, and collagenase to virulence of Bacillus cereus endophthalmitis

Biochemical characterisation of a collagenase from Bacillus cereus strain Q1

Sequence analysis and characterization of the Porphyromonas gingivalis prtC gene, which expresses a novel collagenase activity

Identification and characterization of Helicobacter pylori genes essential for gastric colonization

Pathogenic potential of a collagenase gene from Aeromonas veronii

SlyA regulates the collagenase-mediated cytopathic phenotype in multiresistant Salmonella

Elucidation of structural and functional characteristics of collagenase from Pseudomonas aeruginosa

Identification of protease and rpoN-associated genes of uropathogenic Proteus mirabilis by negative selection in a mouse model of ascending urinary tract infection

Effective antiprotease-antibiotic treatment of experimental anthrax

Identification of collagenase as a critical virulence factor for invasiveness and transmission of pathogenic leptospira species

Discovery of a Potent Inhibitor Class with High Selectivity toward Clostridial Collagenases

N-Aryl-3-mercaptosuccinimides as Antivirulence Agents Targeting Pseudomonas aeruginosa Elastase and Clostridium Collagenases

Phosphonate as a Stable Zinc-Binding Group for "Pathoblocker" Inhibitors of Clostridial Collagenase H (ColH)

Nsaids as a drug repurposing strategy for biofilm control

A COVID-19 drug repurposing strategy through quantitative homological similarities using a topological data analysis-based framework

Computational drug repurposing algorithm targeting trpa1 calcium channel as a potential therapeutic solution for multiple sclerosis

Application of molecular framework-based data-mining method in the search for beta-secretase 1 inhibitors through drug repurposing

Towards direct deposition of bioassay data

DataWarrior: An open-source program for chemistry aware data visualization and analysis

DrugBank: A comprehensive resource for in silico drug discovery and exploration

PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints

The Properties of Known Drugs. 1. Molecular Frameworks

Improving the odds of success in antitumoral drug development using scoring approaches towards heterocyclic scaffolds

SWISS-MODEL: Homology modelling of protein structures and complexes

A Tool to Obtain Structural Guidance in Biocatalytic Investigations

PROCHECK: A program to check the stereochemical quality of protein structures

Verification of protein structures: Patterns of nonbonded atomic interactions

Deviations from Standard Atomic Volumes as a Quality Measure for Protein Crystal Structures

A Method to Identify Protein Sequences That Fold into a Known Three-Dimensional Structure

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Open Babel: An Open chemical toolbox

Approach to estimation and prediction for Normal Boiling Point (NBP) of alkanes based on a novel Molecular Distance-Edge (MDE) vector, γ

Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors

Multiple Mechanisms of Zinc-Mediated Inhibition for the Apoptotic Caspases-3

Effect of pH and denaturants on the fold and metal status of anthrax lethal factor

A Picture of Modern Tc-99m Radiopharmaceuticals: Production, Chemistry, and Applications in Molecular Imaging

Validation of pharmacological protocols for targeted inhibition of canalicular MRP2 activity in hepatocytes using [99mtc]mebrofenin imaging in rats

Ever-Adapting RND Efflux Pumps in Gram-Negative Multidrug-Resistant Pathogens: A Race against Time

QSAR analysis of some 5-amino-2-mercapto-1,3,4-thiadiazole based inhibitors of matrix metalloproteinases and bacterial collagenase

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.