# SAND: a comprehensive annotation of class D β-lactamases using structural alignment-based numbering

Fedaa Attana, Soobin Kim, James Spencer, Bogdan I. Iorga, Jean-Denis Docquier, Gian Maria Rossolini, Mariagrazia Perilli, Gianfranco Amicosante, Alejandro Vila, Sergei Vakulenko, et al.

► To cite this version:

Fedaa Attana, Soobin Kim, James Spencer, Bogdan I. Iorga, Jean-Denis Docquier, et al. "SAND: a comprehensive annotation of class D β-lactamases using structural alignment-based numbering." Antimicrobial Agents and Chemotherapy, 2025, 69 (7), pp.e00150-25. 10.1128/aac.00150-25. hal-05345329

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# SAND: A Comprehensive Annotation of Class D β-lactamases Using Structural Alignment-based Numbering

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Fedaa Attana¹, Soobin Kim¹, James Spencer², Bogdan I Iorga³, Jean-Denis Docquier⁴, Gian

Maria Rossolini⁵, Mariagrazia Perilli⁶, Gianfranco Amicosante⁶, Alejandro J. Vila⁷, Sergei B.

Vakulenko⁸, Shahriar Mobashery⁸, Patricia Bradford⁹, Karen Bush¹⁰, Sally R. Partridge¹¹,

Andrea M. Hujer¹²,¹³, Kristine M. Hujer¹²,¹³, Robert A. Bonomo¹²,¹³,¹⁴,¹⁵,¹⁶*, Shozeb

Haider¹,¹⁷,¹⁸*

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¹ UCL School of Pharmacy, University College London, London WC1N 1AX, U.K.

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² School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, U.K.

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³ Institut de Chimie des Substances Naturelles, Université Paris-Saclay, Gif-sur-Yvette, France

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⁴ Dipartimento di Biotecnologie Mediche, Università di Siena, I-53100 Siena, Italy

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⁵ Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy

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⁶ Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, 67100

L'Aquila, Italy

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⁷ Instituto de Biología Molecular y Celular de Rosario (CONICET IBR -UNR), Rosario, Argentina

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⁸ Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA

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⁹ Antimicrobial Development Specialists, LLC, Nyack, NY, USA

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¹⁰ Department of Biology, Indiana University, Bloomington, Indiana, USA

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¹¹ Sydney School of Medicine, University of Sydney, Sydney, New South Wales, Australia

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¹² Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH,

USA

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¹³ Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH, USA

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¹⁴ Department of Molecular Biology and Microbiology, Case Western Reserve University School of

Medicine, Cleveland, OH, USA

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¹⁵ Departments of Pharmacology, Biochemistry, and Proteomics and Bioinformatics, Case Western

Reserve University School of Medicine, Cleveland, OH, USA

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¹⁶ CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES)

Cleveland, OH, USA

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¹⁷ University of Tabuk (PFSCBR), Tabuk, Saudi Arabia

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¹⁸ UCL Centre for Advanced Research in Computing, University College London, WC1H 9RL, U.K.

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35 **Corresponding author email address**

36 robert.bonomo@va.gov

37 shozeb.haider@ucl.ac.uk

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40 **Keywords**

41 Class D, OXA, β-lactamases, SAND, structure-based sequence alignment, secondary

42 structure annotation

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44 **Abstract**

45 Class D β-lactamases are a diverse group of enzymes that contribute to antibiotic resistance
46 by inactivating β-lactam antibiotics. Examination of class D β-lactamases has evolved
47 significantly over the years, with advancements in molecular biology and structural analysis
48 providing deeper insights into their mechanisms of action and variation in specificity.

49 However, one of the challenges in the field is the inconsistent residue numbering and
50 secondary structure annotation across different studies, which complicates the comparison
51 and interpretation of data. To address this, we propose SAND - a standardized naming
52 system for both residues and secondary structure elements, based on a comprehensive
53 structural alignment of all documented sequences and experimentally obtained crystal
54 structures of class D β-lactamases. This unified framework will streamline cross-study
55 comparisons and enhance data interpretation. Moreover, the standardized framework will
56 enable AI-driven natural language processing (NLP) techniques to efficiently mine and
57 compile relevant data from scientific literature, speeding up the discovery process and
58 contributing to more rapid advancements in β-lactamase research.

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## Consensus Residues Numbering

Class D β-lactamases (DBLs) form a large family with nearly 1300 identified members in β-Lactamase Database (Fig.S1, www.bldb.eu¹) They are produced mainly by Gram-negative bacteria, with some by Gram-positive bacteria.² DBLs are an important mechanism of resistance to β-lactam antibiotics including carbapenems, one of last resort treatments to infections in hospitalised patients.³ The production of class D β-lactamases is a major public health concern, as they can often transfer between different bacterial species, allowing resistance to spread quickly and widely. Some members were found to be membrane bound and are secreted via outer membrane vesicles (OMVs) to contribute further in antimicrobial resistance dissemination. ⁴ Their mechanism of action involves a catalytic serine, as in classes A, and C β-lactamases, since all share the conserved SXXK tetrad and KXG triad.³

Class D enzymes have attracted molecular research to resolve their 3D structures and understand their mechanism of action that uniquely involves carbamylation of the active site lysine. ⁵,⁶ Several mutations were introduced to identify the residues essential for function
and enzyme stability.⁷,⁸,⁹ Recently, a new mechanism of β-lactam deactivation via β-lactone
formation was described by Aertker and Lohan in their studies on OXA-48 and OXA-23 as
well as their V120L and V128L mutants respectively.¹⁰,¹¹ Moreover, publications related to
the design of novel β-lactamase inhibitors continue to grow in number.¹²⁻¹⁵ Notably, all the
studies cited above have used the “sequential” residue numbering for each of the individual
enzymes studied, although there was a proposal of a consensus numbering for class D β-
lactamases (DBL numbering) by Couture et al in 1991.¹⁶ At the time, only five class D
enzymes were known, and numbering was based on multiple sequence alignment, with
Couture et al. assigning numbers to the positions in the consensus alignment without
counting gaps. However, the number of known class D enzymes now exceeds 1300, with
sequence similarity of as little as 14.4% (BLDB 2024). Therefore, we propose an updated
numbering system that accommodates longer insertions and is easy to implement by early
researchers in order to guarantee assigning the same numbering to homologous residues in
upcoming publications.

In this work, we performed structure-based sequence alignment with the UCSF Chimera
Matchmaker module¹⁸ to superimpose and align representative structures from each class D
subfamily listed in Protein Data Bank PDB to exclusively use structures resolved
experimentally. ¹⁹ The resulting alignment was manually optimised and then used to
construct Hidden Markov Model (HMM) profile via the HMMER suite, a widely used tool for
creating profiles based on sequence alignments. ²⁰ A list of structures is provided in Table
ST2 and residue by residue alignment in Table ST3. We selected OXA-48, an extensively
studied carbapenemase, as the reference sequence for assigning standardized residue
numbers. This sequence was chosen because it naturally features the catalytic serine
residue at position 70, aligning with previous literature that adopted the same consensus of
class A β-lactamases. ²¹

The HMM profile enables alignment of query sequences with the OXA-48 reference.
Homologous residues in the query sequence are aligned to the consensus, and each
residue is assigned its standard number. Insertions relative to OXA-48 are labelled with
appended lowercase letters (e.g., 216a, 216b), while deletions will result in numbers being
skipped (See example in Fig. 1). This convention has been applied to and is consistent with
β-lactamases of classes A, B, and C. ²²⁻²⁴ To facilitate adoption of the scheme by
researchers from all backgrounds, we automated the process to run via Python-based
workflow that can be accessed online using GoogleColab.²⁵ A detailed tutorial guide has
been included in the Supplementary Information S4). Alternatively, the workflow can be run
locally using a Jupyter-Notebook²⁶ and requires installation of the required packages on a
local machine.

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Users can upload a FASTA file containing their query sequence to obtain a detailed output in
table format, where each residue is aligned to OXA-48 and annotated with standard
numbering. Detailed step by step instructions and remarks are provided in Tutorial S4. The
numbering system is also being implemented in β-Lactamase Database (bldb.eu) for
broader usability. Table ST5 lists all DBL sequences in the database (as downloaded in
September 2024) aligned to the hmm profile.

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red. Positions identified to be highly conserved, based on alignment of latest set of DBLs
listed on bldb.eu, are highlighted with an asterisk on top.

## Secondary Structure Annotation

All class D β-lactamases share an α-β-α sandwich architecture with minimal deviation in the core structure. Structural variation arises primarily from insertions and deletions, particularly in the loops connecting the core elements. In the mature enzymes (i.e., sequences generated after post-translational removal of the signal peptide responsible for periplasmic export), some subfamilies feature an N-terminal β-strand. This additional strand forms backbone hydrogen bonds with the central β-sheet, effectively extending it within the protein structure. In other subfamilies, the N-terminus instead forms a short coil preceding the first α-helix, resulting in a central β-sheet composed of only six strands. Sometimes, structures of same enzyme resolved under different experimental conditions vary, as in the case of OXA-23.²⁸⁻³⁰ These variations also contribute to inconsistencies in the naming of secondary structural elements across different enzymes in the literature. Moreover, naming of α-helices has been a recurring source of ambiguity, complicating cross-comparisons of structural data. For instance, work by Toth et al. resolved the crystal structure of OXA-143 (PDB: 5IY2) and identified 10 α-helices, 10 β-strands and one 3₁₀-helix excluded from sequential numbering. They referred to the Ω-loop as the loop between helix α6 and helix α8.³¹ On the other hand, Antunes et al., analyzing the same PDB structure, reported 11 α-helices and 6 antiparallel β-strands (numbered starting from β2) and described the Ω-loop as located between helix G and helix I (the seventh and eighth helices) as they assigned ABC nomenclature to the helices.³² Such discrepancies highlight the need for a unified nomenclature for structurally homologous regions in class D β-lactamases (DBLs).

To address this, we conducted a structure-based alignment of representative DBLs whose structures have been resolved experimentally (a total of 21 enzymes). Secondary structures were annotated using ChimeraX ¹⁸, which employs the commonly used Dictionary of Secondary Structure of Proteins (DSSP) algorithm to assign residues to α-helices and β-strands based on hydrogen-bonding patterns. ³³
In our consensus annotation, β-strands and both α/3₁₀ helices were numbered sequentially using the prefixes B and H, starting from 1. This alignment yielded a consensus of 7 β-strands and 11 helices. Notably, the P-loop and Ω-loop, in many DBLs, include a 3₁₀ helix, designated as H4 and H8, respectively. In cases where an additional 3₁₀ helix is observed, a letter suffix is added to the helix name in the consensus annotation (e.g., H4a, H4b). Assignment of secondary structure using other algorithms (eg. STRIDE) has resulted in identification of some short β-strands in long loops. Such structures have been reported earlier to have 10 β-strands.³¹ To test the stability of these short strands, we further conducted temporal structural analyses using the STRIDE algorithm, that combines the use of hydrogen bond energy and statistically derived backbone torsional angle information when assigning secondary structure.³⁴ Tracking these secondary structure elements over the course of 20 µs molecular dynamics simulation time reveals that these are transient structural elements and are not detected by conventional structural analysis tools (Fig.S3). To accommodate such elements in our annotation scheme, we advise annotating them with lower case b for beta strands and lowercase h for helices followed by the numbers of the adjacent consensus elements. For instance, the strands near the edges of P-loop should be called b34 and b45 as they lie between H3/H4 and H4/H5 respectively. When individual elements are absent in certain enzymes, they are omitted without altering the overall numbering order. Detailed illustrations of the consensus annotation and alignment are provided in Fig 2 and Fig.S2.

<figure><img src="image_2.png" /><figcaption>(B)</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>(C)</figcaption></figure>

<table><thead><tr><th>Residue Range</th><th>S5</th><th>Residue Count</th><th>Remarks</th></tr></thead><tbody><tr><td>W25-E27</td><td>B1</td><td>3</td><td></td></tr><tr><td>K29-H38</td><td>H1</td><td>10</td><td></td></tr><tr><td>G42-N48</td><td>B2</td><td>7</td><td></td></tr><tr><td>Q53-T56</td><td>B3</td><td>4</td><td></td></tr><tr><td>R61-N63</td><td>H2</td><td>3</td><td></td></tr><tr><td>A69-D82</td><td>H3</td><td>14</td><td>SXXX</td></tr><tr><td>L83-I102</td><td>COIL</td><td>20</td><td></td></tr><tr><td>A103-W105</td><td>H4</td><td>3</td><td>P-Loop</td></tr><tr><td>N106-N110</td><td>COIL</td><td>5</td><td></td></tr><tr><td>L111-K116</td><td>H5</td><td>6</td><td></td></tr><tr><td>V120-I130</td><td>H6</td><td>11</td><td>SXV</td></tr><tr><td>E132-A141</td><td>H7</td><td>10</td><td></td></tr><tr><td>F142-S155</td><td>COIL</td><td>14</td><td></td></tr><tr><td>F156-D159</td><td>H8</td><td>4</td><td>Ω-Loop</td></tr><tr><td>G160-S165</td><td>COIL</td><td>6</td><td></td></tr><tr><td>A166-Y177</td><td>H9</td><td>12</td><td></td></tr><tr><td>E185-A194</td><td>H10</td><td>10</td><td></td></tr><tr><td>L196-A199</td><td>B4</td><td>4</td><td></td></tr><tr><td>I204-S212</td><td>B5</td><td>9</td><td></td></tr><tr><td>T213-K218</td><td>COIL</td><td>6</td><td>B5-B6 Loop</td></tr><tr><td>I219-E227</td><td>B6</td><td>10</td><td></td></tr><tr><td>V232-D240</td><td>B7</td><td>9</td><td></td></tr><tr><td>S244-Q260</td><td>H11</td><td>17</td><td></td></tr></tbody></table>

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**Fig 2. Secondary Structure Assignment for Class D β-Lactamases.** (A) Flat diagram with consensus annotation of secondary structure elements of Class-D β-lactamases. Putative variable elements (shown in light grey) represent assignments that are transient or not conserved across majority of the Class-D β-lactamase family. (B) Secondary structure annotation mapped onto 3D structure of OXA-48 (PDB ID: 5DTK²¹). (C) Table with residues bordering major secondary structure elements of OXA-48 as assigned according to DSSP algorithm. An illustration of the secondary structural alignment of representative DBLs can be found in Fig. S2.

## Conclusions

In the context of enzyme families with extensive sequence diversity, such as class D β-lactamases, a unified residue numbering scheme is essential for facilitating residue-specific comparisons across studies. A consistent approach that integrates sequence and secondary structure elements is particularly important in the era of advanced Natural Language Processing (NLP) and biomedical literature mining. These technologies have become invaluable in accelerating drug discovery by enabling the efficient extraction and analysis of vast scientific datasets, especially in rapidly evolving fields like antimicrobial resistance (AMR) and pandemic research.³⁵,³⁶

We emphasise that the scheme proposed here, termed SAND (Structural Alignment-based Numbering of DBLs), is not intended to replace the unique residue numbering of individual proteins. Instead, it serves as a standardized reference point for molecular studies, enabling the straightforward comparison of homologous residues across enzymes and studies, particularly in figures and tables that highlight key residues or interactions. Alongside, we provide a consensus annotation to label the conserved secondary structure elements across
this class of enzymes, addressing inconsistencies caused by variations in structure
determination and assignment algorithms. While our automated workflow reliably identifies
conserved motifs and aligns them to the reference in a simple output file, we acknowledge
that the precise boundaries or identification of transient or additional secondary structure
elements may differ. To ensure flexibility, our approach allows researchers to adopt the
assignment method that best suits their needs while maintaining consistency with the
broader literature. By simplifying cross-study comparisons and emphasizing conserved
motifs, the SAND system aims to streamline research on DBLs and enhance accessibility by
researchers from different disciplines.

241

242 Conflict of interest:

243 RAB receives research grants from Merck, Wockhardt, Achaogen, Shionogi,
244 GlaxoSmithKline, Roche, Allecra, VenatoRx, and Entasis. KB receives retirement income
245 from Bristol-Myers Squibb, Johnson & Johnson and Pfizer; is a shareholder for Entasis,
246 Fedora and Johnson & Johnson. All other authors declare no conflict of interest.

247

248 Supplementary information

249 – S1: Supplementary Figures (S1_supplementary_figures.pdf)

250 – ST2: List of structures of structures used in this study (ST2_table_structures.xlsx)

251 – ST3: Structure based sequence Alignment with secondary structure annotations
252 (ST3_tableAlignment.xlsx)

253 – S4: Tutorial: a guide to use the automated workflow to assign SAND (S4_tutorial.pdf)

254 – ST5: DBLs sequences aligned to the built HMM profile (ST5_table_DBLs.xlsx)

255 – S6: Zip folder containing all necessary files to run assignment workflow locally on Linux
256 (local_assign.zip)

257

258 Data Availability

259 All data for supplementary information including the code, tutorial and sample files can be
downloaded from https://github.com/shozebhaider/OXA-SAND

261
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