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

Area of Science:

• Biochemistry
• Structural Biology
• Microbiology

Background:

• Class D β-lactamases are crucial enzymes conferring antibiotic resistance by degrading β-lactam antibiotics.
• Previous research has advanced understanding of their mechanisms and specificity through molecular biology and structural analysis.
• Inconsistent residue numbering and secondary structure annotations hinder data comparison and interpretation across studies.

Purpose of the Study:

• To introduce SAND (Standardized ANnotation system for beta-lactamase Domains), a novel naming system for residues and secondary structures.
• To establish a unified framework for class D β-lactamase data, facilitating cross-study comparisons and interpretation.
• To enable efficient data mining using AI-driven natural language processing (NLP) for accelerated research.

Main Methods:

• Comprehensive structural alignment of all documented class D β-lactamase sequences.
• Inclusion of experimentally obtained crystal structures for accurate annotation.
• Development of a standardized naming convention for residues and secondary structure elements.

Main Results:

• A proposed standardized naming system (SAND) for class D β-lactamase residues and secondary structures.
• A unified framework that addresses inconsistencies in current annotation practices.
• Facilitation of direct comparison and enhanced interpretation of diverse β-lactamase data.

Conclusions:

• The SAND system provides a crucial tool for standardizing class D β-lactamase research.
• This standardization will streamline data integration and interpretation, accelerating the discovery of new insights.
• The framework is expected to enhance AI-driven analysis of scientific literature, speeding up advancements in antibiotic resistance research.