SENSAAS-Flex: a joint optimization approach for aligning 3D shapes and exploring the molecular conformation space

Area of Science:

• Computational chemistry
• Structural biology
• Cheminformatics

Background:

• Traditional shape-based alignment methods rely on conformational ensembles, which are computationally intensive to generate.
• The initial generation of conformer libraries limits the efficiency of molecular alignment processes.

Purpose of the Study:

• To develop a novel method for flexible molecular shape alignment by optimizing 3D conformations.
• To introduce SENSAAS-Flex, an extension of the SENSAAS tool, designed to enhance flexible alignment capabilities.

Main Methods:

• SENSAAS-Flex performs flexible alignment by optimizing molecular conformations iteratively.
• It utilizes a limited set of initial conformers and refines them at the substructure level, constrained by the target shape.
• The method employs a point-based representation of molecular surfaces for shape constraint.

Main Results:

• SENSAAS-Flex successfully reproduced crystal structure geometries in self- and cross-alignment experiments.
• Validation was performed using the AstraZeneca Molecule Overlay Test set and the PDBbind refined dataset.
• The study confirmed the suitability of point-based molecular surface representation for conformational sampling and flexible alignment.

Conclusions:

• SENSAAS-Flex offers an efficient and effective approach to flexible molecular shape alignment.
• The method addresses the computational limitations of generating extensive conformer libraries.
• Point-based surface representation is a viable strategy for conformational space sampling in alignment tasks.