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# Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision

Merveille Eguida, Didier Rognan

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Merveille Eguida, Didier Rognan. Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision. Journal of Cheminformatics, 2021, 13 (1), 10.1186/s13321-021-00567-3. hal-03446724

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1 Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis

2 factor binding sites revealed by computer vision.

3

4 Merveille Eguida¹ and Didier Rognan¹,*

5

6 ¹ Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch,

7 France.

8

9

10 * Corresponding author

11 E-mail: rognan@unistra.fr

12

13
14 **ABSTRACT**

15 Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities
16 remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We
17 recently described a new computational approach (ProCare), inspired by numerical image processing, to
18 identify local similarities in fragment-based subpockets. During the validation of the method, we
19 unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor
20 necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis
21 experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT
22 inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could
23 be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise
24 similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is
25 uniquely suited to identify local and unobvious similarities among totally unrelated targets.

26

27 **Keywords:** binding sites, similarity, point cloud registration

28

29
30 INTRODUCTION

31 Among the many possible approaches to structure-based drug design [1, 2], inferring novel ligand
32 properties from the large-scale comparison of their possible binding pockets gains popularity as the
33 repertoire of protein cavities of known three-dimensional (3D) structures (pocketome) is constantly
34 increasing, thereby offering unique opportunities to design ligands while simultaneously considering
35 multiple targets [3]. The term 'pocketome' was first coined in 2004 by An et al. [4] to describe the universe
36 of cavities located at the surface of macromolecules and capable of binding low molecular-weight ligands.

37 A systematic survey of currently available three-dimensional structures [5], suggests that its size is
38 estimated to ca. 250,000 pockets [6] out of which 10-15% are accommodating true drug-like compounds
39 [7, 8]. Pocket locations can be inferred from the position of already-bound molecules or predicted on the
40 fly, by one of the many available cavity detection methods [3, 9]. The pockeome space can then be
41 searched by numerous computational tools [10] for similarity to any query cavity to predict evolutionary
42 relationships and protein-ligand interactions [3]. The later application is notably of paramount importance
43 to the drug discovery field as it may reveal hidden relationships for guiding the design of safer drug
44 candidates with a precise control of selectivity [3] with respect to either on-targets (polypharmacology
45 approach) [11] or off-targets (side effects mitigation) [12], in a time and cost-effective manner [13].

46 Currently available methods are generally able to detect global similarities between two druggable pockets
47 from different proteins, and therefore permit to transfer drug-like compounds from one target space to
48 another [3]. Identifying more subtle local similarities at the level of fragment-bound pockets remains a
49 much more difficult problem [14] as it requires a searchable archive of fragment-bound subpockets [15-
50 17] and a computational method focusing on local subpocket descriptors. Consequently, there are still
51 very few reports of experimentally verified subpocket similarity examples that have enabled the transfer
52 of chemical fragments across unrelated proteins [18]. To fill the need for local similarity searching methods
53 while comparing pockets of different sizes, we developed a novel method (ProCare) [17] relying on point
cloud registration, a numerical image processing to find a spatial transformation (e.g., scaling, rotation and translation) that aligns two point clouds [19, 20]. ProCare uses as input a point cloud representation of the protein pocket or subpockets, where each point is annotated by eight possible pharmacophoric features (hydrophobic, aromatic, H-bond donor, H-bond acceptor, H-bond donor and acceptor, positive, negative, dummy) complementary to that of the pocket microenvironment [21]. Since ProCare uses local descriptors to compare and align binding subpockets, the method is particularly suited to fragment-based design strategies aimed at positioning fragments in subpockets of any druggable cavity.

While validating the method by focused benchmarking studies [17], we noticed some unexpected local similarity between subpockets from two unrelated proteins with 23% sequence identity: human tumor necrosis factor alpha (TNF-α) trimer [22] and human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) [23]. On the one hand side, TNF-α is a homotrimeric pro-inflammatory cytokine involved in autoimmune disorders such as rheumatoid arthritis and Crohn's disease [24]. It is currently targeted by monoclonal antibodies preventing its recognition by TNF-α receptors (TNFR1 and TNFR2). To date, no small molecule TNF-α inhibitor has reached the market [22]. On the other side, HIV-1 RT is an enzyme used by the HIV virus to replicate its genome by first generating a complementary DNA from the viral RNA template. HIV-1 RT can be blocked by many marketed drugs [25] binding to either the catalytic site (nucleoside inhibitors, e.g. zidovudine) or a remote allosteric pocket (non-nucleoside inhibitors, e.g. efavirenz).

To exclude potential artifacts or biases and provide a strong statistical support to this initial prediction, we here systematically compared the inner cavity of three inhibitor-bound TNF-α trimer structures with 122 non-nucleoside inhibitor-bound HIV-1 RT X-ray structures. In a large majority of pairwise comparisons, the corresponding subpockets were deemed similar, a prediction that could be confirmed by biophysical experiments evidencing a direct micromolar binding of non-nucleoside HIV-1 RT inhibitors to human soluble TNF-α. Interestingly, this unexpected similarity could not be recovered by state-of-the-art cavity
comparisons tools suggesting the unique ability of ProCare to delineate subtle local relationships between unrelated target cavities.

80

## 81 RESULTS AND DISCUSSION

Identifying similarity between pockets from different proteins suggests that the latter might bind to similar molecules. Although molecular recognition is a dynamic and complex process, the above hypothesis is worth investigating in drug design for hit discovery or for potential off-targets detection. We previously described ProCare [17], a novel computational method relying on a point cloud registration algorithm [19, 20] to assess the similarity between protein pockets. ProCare computes and uses local descriptors, which makes it particularly suitable for detecting local similarities among cavities of different sizes. Typically, ProCare aligns the cavities, described by a cloud of 3D points labeled with pharmacophoric features, by comparing the point descriptors and then derives a similarity score. In the current study (see flowchart in Figure 1), ProCare was used to detect local similarities between the full cavity of the target protein (here the inner core of the TNF-α trimer) and a collection of 31,570 subpockets from the sc-PDB dataset [8], a repository of 16,034 protein-ligand complexes of known three-dimensional structure for which the ligand is a pharmacological agent bound to a druggable cavity. First, the full cavity of the target protein is computed with the in-house VolSite algorithm [21] and represented by a cloud of pharmacophore-annotated points (Figure 1). In parallel, the collection of subpocket point clouds is generated after fragmentation of each protein-bound sc-PDB ligand and consideration of the immediate vicinity (4 Å) of generated fragments. Last, the ProCare method aligns and computes the pairwise similarity between the target point cloud, and that from subpockets from the sc-PDB archive (Figure 1). When a statistically significant similarity is found between a subpocket and the target cavity, the transformation matrix used for the previous alignment is then applied to the corresponding and hidden bound fragment that is directly
positioned in the target cavity. In absence of major clashes, the corresponding fragment can therefore be
used for a fragment growing or linking strategy or even directly tested for binding to the target.

While benchmarking the ProCare method, we noticed unexpected high similarities (ProCare score > 0.47;
p-value < 0.05) between the core pocket at the interface of an inhibitor-bound asymmetric human TNF-α
trimer (PDB ID 6OOY) [22], and several non-nucleoside binding sites of inhibitor-bound HIV-1 RT
(Supporting Table S1). Notably, seven subpockets from the HIV-1 RT were ranked among the 100 top
scoring subpockets, with high ProCare similarity scores (ranging from 0.67 to 0.72) corresponding to very
low p-values (from 2.5x10⁻⁴ to 2.1x10⁻⁵).

<figure><img src="image_2.png" /><figcaption>Fig. 1 Virtual screening of sc-PDB subpockets for similarity to the core cavity TNF-α. The inner pocket of TNF-α (PDB ID 6OOY) is converted as a cloud of points with pharmacophoric properties (orange: hydrophobic and aromatic, blue: H-bond donor and positive ionizable, red: H-bond acceptor, H-bond donor and acceptor, and negative ionizable, white: dummy) and compared to the corresponding point clouds originating from fragment-bound subpockets of sc-PDB ligands.</figcaption></figure>

To assess that the predicted similarity between these unrelated binding sites was not fortuitous, we
computed the Receiver-Operating Characteristic (ROC) curve of a binary classifier for which all cavities of
118 a single sc-PDB target (Table 1) are artificially annotated as positives, the rest being defined as negatives.
119 For each target, the ROC curve was defined from the full list of sorted ProCare similarity scores by plotting
120 the true positive rate versus the false positive rate at different threshold settings (Supporting Figure S1).
121 The area under the ROC curve (ROCAUC) provides a statistical estimation of the accuracy of the classifier
122 to discriminate positives from negatives and therefore predict whether the samples from one particular
123 target are similar (or not) to the TNF-α cavity (Table 1).

<table><caption>124 Table 1 Area under the ROC curve of pairwise ProCare similarity scores.ª</caption><thead><tr><td>Target</td><td>Site</td><td>Number of subpockets<sup>b</sup></td><td>ROCAUC</td></tr></thead><tbody><tr><td>HIV-1 RT</td><td>non-nucleoside</td><td>195 (122)</td><td>0.84</td></tr><tr><td>β2 adrenergic receptor</td><td>orthosteric</td><td>14 (14)</td><td>0.35</td></tr><tr><td>Carbonic anhydrase II</td><td>catalytic</td><td>183 (137)</td><td>0.38</td></tr><tr><td>Cyclin-dependent kinase 2</td><td>catalytic</td><td>461 (274)</td><td>0.63</td></tr><tr><td>Heat shock protein 90α</td><td>catalytic</td><td>214 (117)</td><td>0.64</td></tr><tr><td>Thrombin</td><td>catalytic</td><td>253 (126)</td><td>0.35</td></tr></tbody></table>

125 a For each target, the similarity scores of the corresponding subpockets (actives) and decoys (any other
126 subpocket) to the TNF-α query (PDB ID 6OOY) are used to compute the area under the ROC curve.

127 b Total number of subpockets for the corresponding target. The number of PDB entries are in brackets.

128
129 Making the hypothesis that the HIV-1 RT non-nucleoside binding pocket is similar to that of TNF-α, the
130 ProCare score nicely discriminates positives (HIV-1 RT) from decoys (all other sc-PDB cavities) with a
131 ROCAUC value (0.84) well above the threshold corresponding to a random classification, ROCAUC=0.50).
132 Repeating the same exercise with five randomly picked targets (β2 adrenergic receptor, carbonic
133 anhydrase II, cyclin-dependent kinase 2, heat shock protein 90α, and thrombin) lead to much poorer ROC
134 AUC values close or even inferior to random classifications (Table 1). To further exclude a potential bias in
135 the ProCare alignment/scoring method due to the reference TNF-α structure (PDB ID 6OOY) and give a
stronger statistical support to our prediction, we systematically compared two additional binding sites
(PDB IDs 6OOZ, 6OP0) from available asymmetric human TNF-α X-ray structures [22] to that of 122 HIV-1
RT structures bound to non-nucleoside inhibitors.

**Exhaustive comparison of TNF-α trimer and HIV-1 reverse transcriptase binding sites.** A ProCare
similarity matrix was built by comparing cavities of three asymmetric TNF-α structures (PDB identifiers
6OOY, 6OOZ and 6OP0) co-crystallized with benzimidazole inhibitors to the 195 subpockets from 122 non-
nucleoside HIV-1 RT inhibitors binding sites (Supporting Table S2; Figure 2) available in the sc-PDB. We
observed that 76% of all pairwise comparisons were scored higher than the previously statistically
determined ProCare similarity score threshold of 0.47 [17] (Figure 2A).

<figure><img src="image_3.png" /><figcaption>**Fig. 2 Comparison of TNF-α trimer and HIV-1 RT binding sites with ProCare.** (A) Distribution of pairwise similarity scores (n = 195 x 3). Entries scoring above 0.47 (p-value=0.05; threshold marked by the red</figcaption></figure>
dashed line) are considered similar according to a previous statistical analysis of 2 million pairwise alignments [17]. (B) Aligned residues of TNF-α (chain A: cyan, chain B: dark slate blue, chain C: cornflower blue; PDB code: 6OOZ) to HIV-1 RT (orange, PDB code: 1FKO) after rotation and translation of HIV-1 RT protein with the resulting ProCare alignment matrix. (C) ProCare alignment of efavirenz main fragment (light orange) in the TNF-α trimer pocket and PLANTS docking (transparent orange) in the TNF-α trimer pocket (PDB code: 6OOZ). Edge-to-face aromatic interaction with TYR59 of TNF-α chain A and hydrogen bond with TYR151 of TNF-α chain C are depicted by blue dashed lines.

To exclude the possibility that the predicted similarity is caused by peculiar mutations of the HIV-1 RT non-nucleoside biding site, we also compared pairwise similarities for both wild type and mutated HIV-1 RT pockets, but did not observe significant differences in the percentage of HIV-1 RT pockets predicted similar to that of TNF-α (74% and 82% of similar pockets for wild type and mutants, respectively). We thus conclude that the predicted similarity between pockets from these two unrelated targets is independent on the chosen PDB structures and is not biased by mutations in the HIV-1 RT binding site. Since ProCare yields a transformation matrix to align the compared objects (subpockets onto the target pockets), we herein provided the visual analysis for one entry (efavirenz-bound subpocket) aligned to the TNF-α structure 6OOZ. Pairs of residues of equivalent interaction properties (aromatic, hydrogen bond donor and acceptor, hydrophobic) respectively in TNF-α and HIV-1 RT were nicely matched (Figure 2B) demonstrating that the similarity caught with the point clouds is truly present at the residue level. Matched TNF-α/HIV-1 RT residues were: LEU57.A/LEU100; TYR59.A/TYR318; ILE155.A/LEU234; LEU157.A/TRP229; LEU57.B/PHE227; LEU57.C/TYR188; TYR59.C/TYR181 and TYR151.C/TYR181. Inspection of the matched pharmacophoric points that are contributing to the ProCare score showed a mixed contribution of aromatic, hydrogen bond donor and hydrophobic points (Supporting Figure S2) in agreement with the aligned residues (Figure 2B) and the statistics of the contributions of the eight pharmacophoric features to the detected similarity (Supporting Figure S3). Furthermore, efavirenz was docked into TNF-α binding site 6OOZ with PLANTS [26] after validation of the docking protocol by self-docking of the cocrystallized ligand UCB-5307 in 6OOZ (RMSD of top-ranked pose by ChemPLP to crystal coordinates: 0.47 Å, ChemPLP score of -124.79). The ProCare-aligned efavirenz fragment (Figure 3B) in TNF-α fitted well with one of the
PLANTS docking solutions (ranked 3rd/10 with a ChemPLP score of -79.32), corresponding to a RMSD of 1.8 Å of efavirenz main fragment heavy atoms to the ProCare pose (Figure 2C). Aside the potential hydrophobic interactions in the TNF-α binding site, efavirenz docking pose displayed an edge-to-face aromatic interaction with residue TYR59.A and a hydrogen bond with TYR151.C. Interestingly, efavirenz bound to HIV-1 RT protein structure (1FKO) exhibits an edge-to-face aromatic interaction with residue TYR318 [27] (Supporting Figure S4A) that was matched by ProCare to TYR59.A in TNF-α (Figure 2B). Both TYR59.A and TYR151.C are key residues [22] involved in the micromolar and nanomolar binding of the co-crystallized ligands UCB-6876, UCB-5307 and UCB-9260 (Figure 3) in the TNF-α structures 6OOY, 6OOZ, 6OP0; the interaction between TYR151.C residue and the benzimidazole moiety being a hydrogen bond (Supporting Figure S4B). Altogether, these observations are strongly suggesting that subpockets in the non-nucleoside binding site of HIV-1 RT are similar to the TNF-α trimer cavity.

<figure><img src="image_4.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_5.png" /></figure>

**Fig. 3** Structures of TNF-α and HIV-1 RT non-nucleoside inhibitors. (A) TNF-α inhibitors and (B) HIV-1 RT non-nucleoside inhibitors (PDB entries between brackets). Red substructures indicate the main fragment binding to the HIV-1 RT subpocket found similar to the TNF-α cavity.
190
191 Assuming that similar binding sites should accommodate similar ligands, HIV-1 RT non-nucleoside
192 inhibitors should therefore bind to TNF-α. In order to prioritize HIV-1 RT inhibitors for experimental
193 validation of our hypothesis, we checked which inhibitors were bound to the HIV-RT subpockets that are
194 predicted by ProCare as the most similar to the TNF-α cavity (Table 2).

195 Among the corresponding inhibitors, two compounds (Q27097507, TNK6-51) are not commercially
196 available and were not considered. However, two easily purchasable FDA-approved drugs (efavirenz,
197 nevirapine; Figure 3) are almost entirely buried in the HIV-1 RT subpockets found similar to the TNF-α
198 cavity, exhibit a size and molecular volume similar to that of two TNF-α inhibitors (UCB-6876 and UCB-
199 5307; Figure 3) and were therefore selected for biological evaluation. In addition, we also considered a
200 third marketed inhibitor (delavirdine; Table 2, Figure 3) whose pocket was found much less similar to that
201 of TNF-α, although just above the 0.47 ProCare similarity threshold.

<table><caption>202 Table 2 Bound inhibitors of the HIV-1 reverse transcriptase cavities found similar to TNF-α cavities</caption><thead><tr><th>HIV-RT Inhibitor<sup>a</sup></th><th>HIV1-RT PDB entry</th><th>TNF-α PDB entry</th><th>ProCare score</th><th>Rank</th></tr></thead><tbody><tr><td>NNI (Q27097507)</td><td>2VG7</td><td>600Z</td><td>0.810</td><td>1</td></tr><tr><td>EFZ (Efavirenz)</td><td>1FKO</td><td>600Z</td><td>0.773</td><td>2</td></tr><tr><td>NVP (Nevirapine)</td><td>1LWC</td><td>600Z</td><td>0.737</td><td>3</td></tr><tr><td>TNK (TNK-651)</td><td>1S1V</td><td>600Z</td><td>0.731</td><td>4</td></tr><tr><td>NVP (Nevirapine)</td><td>2HNY</td><td>600Z</td><td>0.729</td><td>5</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>SPP (Delavirdine)<sup>b</sup></td><td>1KLM</td><td>600Z</td><td>0.484</td><td>408</td></tr></tbody></table>

<footnote n="203">203 <sup>a</sup> PDB chemical component identifier (Name in brackets).</footnote>

<footnote n="204">204 <sup>b</sup> After manual fragmentation, a higher ProCare score (0.599) was obtained for the subpocket of
205 delavirdine's fragment #2 (Supporting Figure S5) against 600Y pocket (Supporting Table S3).</footnote>
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**Non-nucleoside HIV-1 RT inhibitors bind to human TNF-α.** Three different non-nucleoside FDA-approved drugs (nevirapine, efavirenz and delavirdine) were tested for direct binding to a fluorescent-labelled TNF-α trimer by microscale thermophoresis (MST), a robust and sensitive biophysical method to detect and quantify molecular interactions in solution [28, 29]. The MST signal is based on ligand-dependent temperature-induced changes (thermophoresis, temperature-related fluorescence intensity) of the fluorescence emission of the labelled protein target. The 17.3 kDa homotrimeric TNF-α that spontaneously assemble in solution [30, 31] was therefore labelled by a RED-fluorescent probe for MST experiments in presence of increasing concentrations of the three HIV-1 RT inhibitors (Figure 4).

MST traces in presence of efavirenz and delavirdine showed distinct states (from bound to unbound), indicating a direct interaction of these two inhibitors with TNF-α (Figure 4A, B). Dissociation constants ($K_D$) could be derived for the two corresponding complexes and estimated to 24 ± 8 µM (Efavirenz) and 39 ± 9 µM (Delavirdine), respectively (Figure 4A, B). The measured dissociation constants for the two HIV-1 RT inhibitors are in the same range of magnitude than that of UCB-6876 ($K_D$= 22 µM) [22], one of the three TNF-α inhibitors used as a reference for this study.
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Microscale thermophoresis (MST) demonstrates a direct interaction between HIV-1 RT inhibitors and RED fluorescent-tagged TNF-α. For analysis, the change in thermophoresis is expressed as the change in the normalized fluorescence (ΔF<sub>norm</sub>), which is defined as F<sub>hot</sub>/F<sub>cold</sub> (F-values correspond to average fluorescence values between defined areas marked by the red and blue cursors). Titration of the non-fluorescent ligand results in a gradual change in thermophoresis, which is plotted as ΔF<sub>norm</sub> to yield a binding curve, which can be fitted to derive binding constants. (A) Experimental MST traces of efavirenz (K<sub>D</sub> = 24 ± 8 μM); (B) Experimental MST traces of delavirdine (K<sub>D</sub> = 39 ± 9 μM); (C) Experimental MST traces of nevirapine. Only the best MST traces (highest signal to noise ratio) are shown here. Values for all experiments conducted according to different experimental protocols are listed in **Supporting Table S4**.</figcaption></figure>

Contrarily to our prediction, no thermophoresis signal could be detected in presence of nevirapine (Figure 4C) indicating no binding of this inhibitor to TNF-α, at least in our experimental settings. The herein observations were insensitive to experimental protocols (buffer composition, solubilizing agents, incubation time, MST power; **Supporting Table S4**).

In absence of X-ray structures of TNF-α bound to efavirenz and delavirdine, we cannot rule out the
possibility that both inhibitors bind to a different pocket than that highlighted in the current computational
study. This hypothesis is however very unlikely for two reasons: (i) no other cavity than that occurring at
the inner core of the multimeric TNF-α could be detected among the currently existing 33 structures
240 available in the Protein Data Bank; (ii) all non-covalent small molecular weight inhibitors co-crystallized
241 with TNF-α dimeric or trimeric forms [32–35] are exactly bound at the central pocket examined in this
242 study.

243 We should recall here that none of the HIV-1 RT inhibitors has been optimized for binding to TNF-α and is
244 directly repurposable for treating TNF-α-dependent autoimmune disorders. However, we do think that
245 efavirenz may be optimized to a much more potent HIV-1 RT inhibitor by following a strategy similar to
246 that reported to modify the 22 µM TNF-α inhibitor UCB-6876 to a 9 nM lead (UCB-5307; Figure 3) by just
247 occupying a side pocket formed by the three TYR199 side chains of the TNF-α homotrimer with a pyridyl
248 ring [22]. Structure-guided efavirenz optimization for TNF-α binding is therefore possible by appropriate
249 trimming of unnecessary cyclopropylethynyl substituent and occupation of the above-described potency
250 subpocket.

251
252 The similarity between TNF-α trimer and HIV-1 reverse transcriptase binding sites is not obvious. To
253 demonstrate whether the herein disclosed similarity between the human TNF-α trimer and the HIV-1 RT
254 non-nucleoside binding sites is obvious, we performed the same set of pairwise binding site comparisons,
255 as that previously reported for ProCare (Figure 2), with state-of-the-art methods [10] developed either in-
256 house (FuzCav [36], Shaper [21] and SiteAlign [37]) or by third parties (G-LoSA [38], KRIPO [15] and ProBiS
257 [39]). The binding site perception, comparison algorithm and scoring function is specific to each method.
258 Some methods (FuzCav, SiteAlign) consider entire cavities while some others utilize either fragment-bound
259 subpockets (KRIPO, Shaper) or local protein descriptors (G-LoSA). To make the comparison consistent, the
260 same set of atomic coordinates were compared, a binding site being defined by the protein PDB identifier,
261 the ligand PDB HET record (three alphanumeric character describing non-standard PDB residues), chain
262 identifiers and list of amino acids lining the cavity. The only exception was for the KRIPO method, which
263 used all the chains available in the PDB entry, but still corresponding to the same tuple (PDB, HET) as for
264 the other methods. For each method, the distribution (Figure 5) and percentage of pairwise comparisons
265 scored above the developer's recommended similarity threshold (Table 3) were reported.

<table><caption>266 **Table 3** Comparison of three TNF-α and 122 HIV-1 RT non-nucleoside binding sites by state-of-the-art
cavity comparison methods.</caption><thead><tr><td>Method</td><td>Score threshold<sup>a</sup></td><td>Metric</td><td>Success rate<sup>b</sup></td></tr></thead><tbody><tr><td>G-LoSA</td><td>0.59</td><td>GA-score</td><td>35.2</td></tr><tr><td>KRIPO</td><td>0.50</td><td>Modified Tanimoto coefficient</td><td>5.8</td></tr><tr><td>Shaper</td><td>0.44</td><td>ColorRefTversky</td><td>1.4</td></tr><tr><td>SiteAlign</td><td>0.6, 0.2</td><td>d1 and d2 distances<sup>c</sup></td><td>0.3</td></tr><tr><td>FuzCav</td><td>0.16</td><td>Tanimoto coefficient</td><td>0</td></tr><tr><td>ProBiS</td><td>2</td><td>Z-score<sup>d</sup></td><td>0</td></tr><tr><td>ProCare</td><td>0.47</td><td>ProCare score</td><td>76.6</td></tr></tbody></table>

268 <sup>a</sup> Developer's recommended similarity/distance threshold for estimating two binding sites similar.

269 <sup>b</sup> Percentage of pairwise comparisons scored above the threshold.

270 <sup>c</sup> For SiteAlign comparisons, pairs are considered similar when the two distances (d1, d2) are below the score threshold value [37].

271 <sup>d</sup> The Z-score indicates the statistical relevance of ProBiS binding site alignments.

273

274 Strikingly, only the G-LoSA method relying on a graph-based local alignment of cavity-lining amino acids, managed to find some similarity between the two sets of binding sites, however with reduced success rate (35.2%) when compared to the ProCare algorithm (76.6 % success rate; **Table 3**). We acknowledge that the developer's recommended thresholds may be biased toward peculiar datasets. However, all methods compared herein were subjected to the same protocol and we do think that the threshold scores are appropriate indicators in a virtual screening setting where there is no room for a one-by-one case study of each pairwise comparison.
<figure><img src="image_7.png" /><figcaption>Fig. 5 Score distribution of pairwise comparisons between binding sites of TNF-α trimer and HIV-1 reverse transcriptase. Binding sites in asymmetric structures of TNF-α trimer (n=3) were compared to binding sites of non-nucleoside inhibitors in HIV-1 reverse transcriptase (sc-PDB set, n=122). Pairs with similarity measures scored above each method-specific threshold (red dashed line) were considered similar. For SiteAlign comparisons, pairs are considered similar in case the two distances (distance 1, distance 2) are below the recommended cut-off. For ProBiS, the threshold above which an alignment is considered significant is marked by the blue dashed line.</figcaption></figure>

The herein reported binding of some HIV-1 RT non-nucleoside inhibitors to human TNF-α remains unobvious to many binding site comparison algorithms. Would this unexpected feature be better captured by remote ligand similarities? To investigate this question, we compared 2D and 3D descriptors of the corresponding inhibitors (Figure 6).
<figure><img src="image_8.png" /><figcaption>**Fig. 6 Pairwise similarity between inhibitors of TNF-α trimer and non-nucleoside inhibitors of HIV-1 reverse transcriptase.** Recently described TNF-α trimer inhibitors (n=3) were compared to non-nucleoside inhibitors of HIV-1 RT (sc-PDB set, n=122). Pairs with similarity measures scored above each descriptor-specific threshold (red dashed line) were considered similar. (Top left) 2D similarity estimated by a Tanimoto metric using Morgan2 circular fingerprint, (Top right) 2D similarity estimated by a Tanimoto metric using 166 MACCS public keys. (Bottom) 3D shape comparison (ROCS) estimated by the Tanimoto Combo metric.</figcaption></figure>

Neither comparing 2D fingerprints nor 3D shapes would have confidently suggested possible binding of HIV-1 RT inhibitors to TNF-α trimer (Figure 6) since none of the considered ligand pairs exhibit a pairwise similarity above an acceptable threshold (Morgan2 circular fingerprint: 0.30 [40]; 166 public MACCS keys: 0.65 [40], Tanimoto Combo ROCS 3D similarity: 1.5 [41, 42]). We should precise here that 3D similarities were inferred from PDB protein-bound ligand X-ray structures and that alternative conformations might be selected by the two targets, although the very rigid efavirenz does indeed bind to the two proteins of interest albeit with different affinities (TNF-α, $K_D=24~\mu M$; HIV-1 RT, ChEMBL median IC₅₀= 20 nM). Extending 2D fingerprint comparisons to additional 2,361 HIV-1 RT inhibitors (Supporting Table S5) from the ChEMBL database [43], did not change our conclusion since only 0.71% and 0.09% of the corresponding pairs were found similar using Morgan2 and 166 public MACCS keys, respectively (data not shown).
313

314 **CONCLUSION**

315 Herein, we describe a systematic comparison of fragment-bound subpockets from *a priori* unrelated
316 targets (TNF-α, HIV-1 RT) but predicted to share local similarities according to our recently-developed
317 ProCare point cloud registration method. The computational prediction was verified by microscale
318 thermophoresis experiments evidencing the micromolar binding of some but not all HIV-1 RT non-
319 nucleoside inhibitors to human soluble TNF-α. Interestingly, the ProCare prediction could not be revealed
320 by other state-of-the-art cavity or ligand similarity search methods. Point cloud registration, a
321 computational method frequently used for digital image processing in robotics and medical imaging,
322 enables the detection of subtle and local protein similarities thanks to a powerful description of subpocket
323 microenvironments. The ProCare method appears as a promising idea generator for drug repurposing and
324 fragment-based ligand design since it is able to pick starting ligands at a proteomic scale.

325

326

327 **METHODS**

328 **Preparation of protein and ligand structures.**

329 **TNF-α structures.** The recently described asymmetric structures of the human TNF-α trimer bound to
330 different inhibitors were retrieved from the RCSB Protein Data Bank (PDB) homepage
331 (https://www.rcsb.org) [44] using the following identifiers: 6OY, 6OP0, 6OOZ [22]. The PDB structures
332 were protonated with Protoss [45] v.4.0, then split into protein, ligands and water molecules and finally
333 converted into mol2 format with Sybyl-X v.2.1.1 (Certara USA, Inc., Princeton, NJ 08540). The binding sites
334 ('SITE') were defined as any protein residue with at least one heavy atom closer than 6.5 Å from any ligand
335 heavy atom and saved in mol2 and pdb formats. The ligands were converted into sdf format with OpenEye
336 Python toolkits v.2020.0.4 (OpenEye Scientific Software, Santa Fe, U.S.A.). Cavities were detected with
337 IChem v.5.2.9 VolSite utility [21] (cavity_all output) using default parameters. The cavity points are labeled
338 with eight possible pharmacophoric features (hydrophobic, aromatic, H-bond donor, H-bond acceptor, H-
339 bond donor and acceptor, positive, negative, dummy) that are complementary to the features of the
340 nearest protein atom. If no protein atom is found within a 4 Å distance of a cavity point, the latter is
341 assigned a dummy property.

342
343 HIV-1 reverse transcriptase PDB structures. Starting from the PDB structure 1VRT as a reference, a search
344 was performed in the RCSB PDB (https://www.rcsb.org) [44] to retrieve all structures with strict matching
345 ("Structure Similarity" query in the PDB). After visual check, 122 entries already available in the sc-PDB
346 repository (http://bioinfo-pharma.u-strasbg.fr/scPDB) [8] and for which the ligand is a non-nucleoside
347 inhibitor were kept. The remaining PDB structures were protonated with Protoss [45] v4.0. The list of the
348 PDB identifiers and Uniprot accession numbers is reported **Supporting Table S2**. According to the sc-PDB
349 preparation rules, the binding sites ('SITE') were defined as described above. Protein, ligand and binding
350 site 'SITE' structures were directly retrieved in mol2 file format from the sc-PDB archive. The corresponding
351 122 ligands were 3D-fragmented with the IChem v.5.2.9 [49] fragmentation utility [47] and the
352 complementary VolSite [21] cavity points, computed at 4 Å around each fragment were finally saved. The
353 ligands were converted into sdf format as described above.

354
355 Preparation of HIV-1 reverse transcriptase ChEMBL ligands. Bioassay information were first retrieved
356 from the ChEMBL [43] dataset (Release 28; https://www.ebi.ac.uk/chembl) by querying the general
357 keyword 'reverse transcriptase' and retaining ChEMBL target identifiers (CHEMBL247, CHEMBL4296301,
358 CHEMBL2366516) corresponding to HIV-1 RT. Ligands with a measured sub-micromolar half-maximal
359 inhibitory concentration (IC50) against the HIV1-RT single target were defined here as inhibitors
360 (Supporting Table S5). The corresponding SMILES strings were retrieved and further processed with RDKit
361 (Open-source cheminformatics; http://www.rdkit.org) v.2019.03.4.0 to remove redundancy.

362
363 **Preparation of sc-PDB fragments and subpockets.** Ligands coordinates from the sc-PDB (http://bioinfo-pharma.u-strasbg.fr/scPDB) [46] v.2016 archive were fragmented in 3D with the IChem v.5.2.9
364 fragmentation utility [47]. Fragmentations occurs in the binding sites so that only the main fragments
365 interacting sufficiently (four interactions of which at least one is polar) with their target proteins were
366 kept. Finally, the cavity pharmacophoric points cloud were computed at 4 Å from the fragments center to
367 describe the protein subpocket, using the IChem v.5.2.9 VolSite utility ("cavity_4" output). VolSite cavities
368 exhibiting less than three points were removed. A total of 31,570 valid fragment-bound subpockets were
369 finally obtained.
370

371
372 **Cavity similarities.**

373 ProCare. ProCare [17] v.0.1.1 pairwise comparison were performed on cavities computed with the VolSite
374 module [21] in IChem v5.2.9 [49]. Entire cavities ("cavity_all" output) were calculated for TNF-α structures
375 whereas only cavity points closer than 4.0 Å from any fragmented ligand center ("cavity_4" output) were
376 considered for sc-PDB subpockets. VolSite cavity points were directly used for point cloud registration
377 staring with determination of colored fast point feature histograms (c-FPFH) as previously described [17].
378 Finally, the respective set of c-FPFH descriptors for the two cavities were compared to each other using a
379 RANSAC algorithm [19, 20] followed by refinement with default parameters [17]. Alignments results were
380 scored with the default ProCare scoring function [17] which evaluates with a Tversky metric the proportion
381 of aligned points of the same pharmacophoric features. In agreement with our previous study [17] where
382 the similarity threshold of 0.47 (p-value of 0.05) was statistically determined, pockets scoring above 0.47
383 were considered similar.

384
385 FuzCav. FuzCav [36], an alignment-free method, was used to compare the binding site 'SITE' (mol2 format)
386 entries of TNF-α dataset to the binding sites of HIV-1 RT sc-PDB dataset. Each binding site was tagged to
387 compute a 4,833 bit-string that count all possible pharmacophoric triplets based on the atomic coordinates
388 of Cα atoms lining the binding cavity. The pairwise comparisons of the fingerprints were evaluated with
389 the default similarity score, with a threshold set at a value of 0.16 to distinguish similar from dissimilar
390 binding sites.

391
392 G-LoSA. G-LoSA [38] v.2.2 is an alignment tool that was used with the binding sites 'SITE' pdb files. G-LoSA
393 computes a set of inter-structural Cα pair distances to derive a graph, which will later be subjected to
394 maximum clique search. The default G-LoSA score (GA-score) was used to evaluate the alignments. A
395 threshold value of 0.59, recommended by the authors [38] and corresponding to a p-value of 0.05, was
396 used to distinguish similar from dissimilar binding sites.

397
398 KRIPO. PDB ligands structural information were downloaded from Ligand Expo (http://ligand-
399 expo.rcsb.org/) and prepared according to the KRIPO procedure (https://github.com/3D-e-Chem/kripo).
400 Then KRIPO [15] v.1.0.1 was used with the list of prepared PDB structures for the pharmacophore fuzzy
401 fingerprints calculations, using default parameters (fragmentation procedure activated). The pairwise
402 similarities of the fingerprints were estimated with kripodb (v.3.0.0) using the modified Tanimoto
403 coefficient as similarity metric. A threshold value of 0.50 was used to distinguish similar from dissimilar
404 binding sites.

405
406 ProBiS. In a first place, the surface information (srf files) was computed for each prepared PDB structures
407 with the default parameters referenced in the manual (3.0 Å to the ligand). ProBiS [39] requires a list of
408 ligand HET code and residue number for each PDB entries. That list was provided to ensure that the
409 ligands/sites considered are the same as in the binding site datasets used for other methods. Then, the
410 alignment and comparison of the srf files were executed with default parameters, except for the Z-score
411 that was set to a high negative value (-9999) as suggested by the authors to enforce the output of all
412 results. Similarity between two binding sites was evaluated by the alignment score and Z- score. A
413 threshold Z-score value of 2.0 was used to distinguish significant from irrelevant binding site alignments.

414
415 SiteAlign. For each entry, the list of natural amino acids in the 'SITE' mol2 files were provided as input.
416 SiteAlign [37] v.4.0 describes a binding site by a polyhedron of 80 discretized triangles annotated with eight
417 possible pharmacophoric features projected from cavity-lining C-α atoms. This results in a fingerprint of
418 640 integers. The pairwise comparisons imply aligning the corresponding polyhedron and computing the
419 d1 and d2 distances of the fingerprints. The distance thresholds of d1=0.6 and d2=0.2 were applied
420 respectively, to discriminate similar from dissimilar binding sites.

421
422 Shaper. Shaper [21] v.1.0 uses the same input files (VolSite cavities in mol2 file format) as ProCare. Shaper
423 is an alignment method based on the OpenEye ShapeTK toolkit (OpenEye Toolkits 2020.2.0, OpenEye
424 Scientific Software, Santa Fe) to maximize the overlap of shape and pharmacophoric features of the two
compared cavities, thanks to a smooth Gaussian function. The alignments were realized with default
settings and scored with a Tversky metric putting more weight on the reference cavity (RefTve). A
threshold RefTve value of 0.44 (p-value = 0.005) was used to distinguish similar from dissimilar binding
sites.

429

430 Ligand similarities

431 Ligand 2D similarity. Morgan fingerprints on the one hand, and 166 public MACSS keys on the other hand
432 were computed on the PDB ligands (sdf format) and ChEMBL ligands (SMILES strings) with RDKit (Open-
433 source cheminformatics; http://www.rdkit.org) python package v.2019.03.4.0 using default parameters
434 (radius = 2 Å for the Morgan fingerprints). The Tanimoto coefficients of the pairwise TNF-α ligands/HIV-1
435 RT ligands fingerprints comparison were reported. Cut-off values of 0.30 (Morgan fingerprints) and 0.65
436 (MACCS keys) were used to discriminate chemically similar from dissimilar ligands.

437

438 Ligand 3D similarity. sc-PDB HIV-1 RT inhibitors were compared to TNF-α inhibitors with OpenEye ROCS
439 v.3.2.0.4 and scored by decreasing Tanimoto similarity metric accounting for both shape and chemical
440 features overlap (TanimotoCombo). A TanimotoCombo cut-off value of 1.5 was used to discriminate
441 chemically similar from dissimilar ligands.

442

443 Docking. TNF-α X-ray structure 6OOZ was prepared as described above (see TNF-α structures). 6OOZ co-
444 crystallized ligand on the one hand, delavirdine, efavirenz and nevirapine as well as their main fragments
445 on the other hand were drawn with MarvinSketch v.16.10.17 (ChemAxon Ltd, 1031 Budapest, Hungary)
446 and saved into 2D sdf format. They were ionized with Filter v.2.5.1.4 (OpenEye Scientific Software, Santa
447 Fe, U.S.A.) using customized parameters (Supporting Table S6). Then Corina v.3.40 (Molecular Networks GmbH, 90411 Nürnberg, Germany) was used to generate a starting 3D conformation for each inhibitor.
448
449 The prepared molecules were docked into the target 6OOZ with PLANTS v.1.2 [26] using the following configuration: the grid was set at 13 Å from the binding site center; poses were searched 'speed1' settings to generate a maximum of 10 poses per ligand using a clustering rmsd of 2 Å. Solutions were scored with the default ChemPLP scoring function [26]. The docking protocol was validated by computing the rmsd between of the docked 6OOZ ligand coordinates and the X-ray coordinates. Results were processed and rescored by computing the interaction fingerprint (IFP) similarity (Tanimoto metric) [48] between X-ray and docking poses. The IFPs were computed with IChem v.5.2.9 IFP module. All poses were visually inspected using Maestro v.2019-3 (Schrödinger, New York, NY 10036-4041).
450
451
452
453
454
455
456

457

**Chemicals and biologicals.** Nevirapine (catalog #S1742), efavirenz (catalog #S4685) and delavirdine mesylate (catalog #S6452) were purchased from Selleck Chemicals (https://www.selleckchem.com/). Soluble human TNF-α (catalog # Z01001) was purchased from GenScript (http://www.genscript.com).
458
459
460

461

**Binding of HIV-1 RT inhibitors to human TNF-α (Microscale thermophoresis).** Human TNF-α was labeled using the RED-NHS 2nd generation labeling kit (NanoTemper Technologies GmbH) using a protein concentration of 10 µM and a **molar dye-to-protein** ratio ~ 3:1. A label/protein ratio of 0.4 was determined using photometry at 650 and 280 nm. Compounds efavirenz, delavirdine and nevirapine were initially dissolved in DMSO to afford stock solutions of 10 mM. These were then diluted to initial concentrations of 260 µM into 20 mM K-phosphate pH 7.4, 150 mM NaCl ensuring a final concentration of DMSO of 2.6 %. These compounds were serially diluted 2:1 in buffer 20 mM K-phosphate pH 7.4, 150 mM NaCl, 2.6 % DMSO producing ligand concentrations ranging from 260 µM to 594 nM (16 titration points). For MST
462
463
464
465
466
467
468
469
measurements, each ligand dilution was mixed with 1 volume of fluorescently-labelled TNF-α at 680 nM in 20 mM K-phosphate pH 7.4, 150 mM NaCl, 0.02% Tween-20, which leads to a final concentration of TNF-α of 340 nM and final ligand concentrations at half of the ranges above. The final buffer is 20 mM K-phosphate pH 7.4, 150 mM NaCl, 0.01% Tween-20 and 1.3 % DMSO. After a 15-min incubation at room temperature in the dark, followed by centrifugation at 13,000 g for 3 min, each solution was filled into Monolith NT Premium capillaries (NanoTemper Technologies GmbH). Thermophoresis was measured at 25°C with 40% LED power and 20%, 40% and 80% MST power using a Monolith NT.115 (NanoTemper Technologies GmbH). Data were analyzed in the NT Analysis software version 1.5.41 (NanoTemper Technologies GmbH).

## 480 LIST OF ABBREVIATIONS

2D: two-dimension

3D: three-dimension

AUC: Area Under the Curve

c-FPFH: colored Fast Point Feature Histogram

DMSO: Dimethyl Sulfoxide

HIV: Human Immunodeficiency Virus

MST: Microscale Thermophoresis

PDB: Protein Data Bank

RANSAC: Random Sample Consensus

RMSD: Root Mean Square Deviation
491 ROC: Receiver Operating Characteristics

492 RT: Reverse Transcriptase

493 TNF: Tumor Necrosis Factor

494

495

496 SUPPLEMENTARY INFORMATION

497 **Figure S1:** Receiver operating characteristic (ROC) curves derived from ProCare similarity scores. **Figure**

498 **S2:** ProCare alignment of efavirenz main fragment subpocket onto TNF-α trimer pocket. **Figure S3:**

499 Contributions of the eight pharmacophoric features to the ProCare similarity score between HIV-1 RT and

500 TNF-α. **Figure S4:** Non-covalent interactions between efavirenz and HIV-1 RT, and between UCB-5307 and

501 TNF-α trimer. **Figure S5:** Manual fragmentation of delavirdine in three fragments (#1 to #3). **Table S1:** sc-

502 PDB subpockets sorted by decreased ProCare similarity to the inner cavity of human TNF-α. **Table S2:** PDB

503 entries describing non-nucleoside inhibitors bound to HIV-1 reverse transcriptase. **Table S3:** Comparison

504 of delavirdine subpockets, resulting from manual fragmentation, with TNF-α trimer pockets. **Table S4:**

505 Dissociation constant (KD) of three HIV-1 RT inhibitor binding to human soluble TNF-α, according to MST

506 experimental conditions. **Table S5:** CHEMBL entries describing HIV-1 RT non-nucleoside inhibitors. **Table**

507 **S6:** Customized rules for OpenEye Filter ionization.

508

509 AVAILABILITY OF DATA AND MATERIALS

510 **Data.** Input and results data are available at https://github.com/kimeguida/ProCare_TNF.
511 **Software.** ProCare, version 0.1.1 and 0.1.0, https://github.com/kimeguida/ProCare; Fuzcav,  
512 http://bioinfo-pharma.u-strasbg.fr/labwebsite/downloads/FuzCav.tgz; G-LoSA, version 2.2,  
513 https://compbio.lehigh.edu/GLoSA; KRIPODB, version 3.0.0, http://3d-e-chem.github.io/kripodb; KRIPO,  
514 version 1.0.1, https://github.com/3D-e-Chem/kripo; ProBiS, http://insilab.org/probis-algorithm/;  
515 SiteAlign, version 4.0, http://bioinfo-pharma.u-strasbg.fr/labwebsite/downloads/SiteAlign-4.0.tgz;  
516 Shaper, version 1.0, http://bioinfo-pharma.u-strasbg.fr/labwebsite/downloads/Shaper.tgz; RDKit python  
517 package, version 2019.03.4.0, https://www.rdkit.org/; ROCS, version 3.2.0.4,  
518 https://www.eyesopen.com/rocs; IChem, version 5.2.9, http://bioinfo-pharma.u-  
519 strasbg.fr/labwebsite/downloads/IChem_v.5.2.9.tgz: Python OpenEye toolkits version 2020.0.4; FILTER,  
520 version 2.5.1.4, https://www.eyesopen.com/filter; PLANTS version 1.2, http://www.tcd.uni-  
521 konstanz.de/plants_download; Python package Matplotlib version 3.0.2; Maestro version 2019-3,  
522 https://www.schrodinger.com/products/maestro; Pymol version 2.1, https://pymol.org/2; Sybyl-X  
523 v.2.1.1, https://www.certara.com/sybyl-x-software; MarvinSketch version 16.10.17,  
524 https://chemaxon.com/products/marvin;

525

526

527 COMPETING INTERESTS

528 The authors declare no competing interests.

529

530 FUNDING

531 The authors are thankful to the Doctoral School of Chemical Sciences (EDSC, University of Strasbourg) for
532 a doctoral fellowship to M.E.
533

534 AUTHOR CONTRIBUTIONS

535 Conceived and design experiments: ME, DR. Performed the experiments: ME, Analyzed the data: ME, DR.

536 Wrote the paper: ME, DR.

537

538 ACKNOWLEDGMENTS

539 The authors are thankful to the Doctoral School of Chemical Sciences (EDSC, University of Strasbourg) for

540 a doctoral fellowship to M.E. The Calculation Center of the IN2P3 (CNRS, Villeurbanne, France) is

541 acknowledged for the allocation of computing time and excellent support. We sincerely thank Prof. M.

542 Rarey (University of Hamburg, Germany) for providing an executable version of Protoss, OpenEye Scientific

543 Software for the generous allocation of an academic license, and Dr. Catherine Birck (IGBMC, Illkirch) for

544 the microscale thermophoresis experiments.

545

546

547 AUTHOR INFORMATION

548 Corresponding author

549 **Didier Rognan** – Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de

550 Strasbourg, F-67400 Illkirch; orcid.org/0000-0002-0577-641X; Email: rognan@unistra.fr

551

552 Author
553 **Merveille Eguida** – Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de
554 Strasbourg, F-67400 Illkirch; orcid.org/0000-0002-0976-0239; Email: keguida@unistra.fr

555

556

557

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