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Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Ligand Binding and Linkage

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...

Metal-Ligand Bonds

Metal-Ligand Bonds

The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...

The Equilibrium Binding Constant and Binding Strength

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:

Complexometric Titration: Ligands

Complexometric Titration: Ligands

Different monodentate and polydentate ligands are used as complexing agents in complexometric titration reactions. The formation of complexes by mono- and bidentate ligands involves two or more intermediate steps, limiting their use as complexing agents. In comparison, polydentate ligands can form complexes with metal ions in a single-step process, facilitating sharper end points. This means polydentate ligands, such as amino carboxylic acid derivatives, are most commonly employed in...

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Classification of Hemilabile Ligands Using Machine Learning.

Ilia Kevlishvili¹, Chenru Duan^1,2, Heather J Kulik^1,2

¹Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

The Journal of Physical Chemistry Letters

|December 5, 2023

Summary

This summary is machine-generated.

Identifying hemilabile ligands is challenging. Machine learning models accurately predict hemilability in ligands by analyzing coordination spheres, improving catalytic applications.

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Area of Science:

Coordination Chemistry
Computational Chemistry
Materials Science

Background:

Hemilabile ligands offer a way to tune catalyst stability and reactivity.
Identifying hemilabile ligands is difficult due to their complex coordination behavior.

Purpose of the Study:

To develop a reliable method for identifying hemilabile ligands.
To utilize machine learning for predicting ligand hemilability.

Main Methods:

Utilized the Cambridge Structural Database to identify ligands with varying denticities.
Implemented a semi-supervised learning approach with a label-spreading algorithm.
Developed machine learning classification models to predict hemilability.

Main Results:

A heuristic based solely on coordinating atom identity is insufficient for identifying hemilabile ligands.
Machine learning models accurately predict hemilability for bi-, tri-, and tetradentate ligands.
Feature importance analysis revealed the significance of the second, third, and fourth coordination spheres.

Conclusions:

Machine learning provides a robust method for identifying hemilabile ligands.
Understanding coordination sphere interactions is crucial for predicting ligand hemilability.
This approach can advance the design of efficient catalysts.