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Noncovalent Attractions in Biomolecules

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

Noncovalent Attractions in Biomolecules

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Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Hydrogen Bonds

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A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...

Hydrogen Bonds

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Hydrogen BondsHydrogen bonds are weak attractions between atoms that have formed other chemical bonds. One of these atoms is electronegative, like oxygen, and has a partial negative charge. The other is a hydrogen atom that has bonded with another electronegative atom and has a partial positive charge.Hydrogen Bonds Control the World!Because hydrogen has very weak electronegativity when it binds with a strongly electronegative atom, such as oxygen or nitrogen, electrons in the bond are...

Conservation of Protein Domains

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Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Related Experiment Video

Updated: Jul 18, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Learning about protein hydrogen bonding by minimizing contrastive divergence.

Alexei A Podtelezhnikov¹, Zoubin Ghahramani, David L Wild

¹Keck Graduate Institute of Applied Life Sciences, Claremont, California 91711, USA.

|November 17, 2006

Summary

This study uses machine learning to analyze protein structures, revealing strong backbone hydrogen bonds are nearly linear and measure 1.1-1.5 kcal/mol. These bonds are crucial, involving a quarter of protein donors and acceptors.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Quantifying hydrogen bond strength and geometry in proteins is historically challenging.
Accurate characterization is vital for understanding protein folding and function.

Purpose of the Study:

To apply a novel machine learning technique, contrastive divergence, for estimating hydrogen bond strength and geometry.
To analyze strong interpeptide backbone hydrogen bonds across diverse protein folds.

Main Methods:

Utilized contrastive divergence, a statistical machine learning approach.
Analyzed a dataset of protein structures with varying folds.
Estimated interatomic energy terms for hydrogen bonds.

Main Results:

Determined strong interpeptide backbone hydrogen bond strengths between 1.1 and 1.5 kcal/mol.
Characterized these bonds as having an almost linear geometry (four atoms involved).
Estimated that approximately 25% of hydrogen bond donors and acceptors participate in these strong bonds.

Conclusions:

The study successfully estimates hydrogen bond strength and geometry using machine learning.
Findings align with previous experimental estimates, validating the approach.
Highlights the significant role of strong backbone hydrogen bonds in protein structure.