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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...

Conserved Binding Sites

Conserved Binding Sites

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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:

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

ISLAND: in-silico proteins binding affinity prediction using sequence information.

Wajid Arshad Abbasi^1,2, Adiba Yaseen³, Fahad Ul Hassan³

¹Computational Biology and Data Analysis Laboratory, Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, Pakistan. wajidarshad@gmail.com.

|December 9, 2020

Summary

This summary is machine-generated.

Predicting protein binding affinity using only sequences is challenging. While current methods fall short, a new tool called ISLAND shows improved accuracy for protein-protein interactions, advancing therapeutic design.

Keywords:

Binding affinity Protein sequence analysis Protein-protein interaction Support vector machines Web services

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational biology
Machine learning in bioinformatics
Drug discovery

Background:

Protein-protein interactions are crucial for biological processes and drug development.
Experimental determination of binding affinity is costly and time-consuming.
Structure-based computational methods are limited to proteins with known structures.

Purpose of the Study:

To explore sequence-based machine learning for protein binding affinity prediction.
To develop a novel, accurate, and accessible computational method for predicting protein binding affinity.

Main Methods:

Utilized protein sequence information, bypassing the need for 3D structures.
Applied machine learning techniques for binding affinity prediction.
Developed and validated a novel sequence-based predictor, ISLAND.

Main Results:

State-of-the-art sequence-only predictors exhibit unsatisfactory generalization performance.
ISLAND demonstrates superior accuracy compared to existing methods on validation and independent test datasets.
A cloud-based webserver and Python code for ISLAND are publicly available.

Conclusions:

Accurate, sequence-based prediction of protein binding affinity remains an open challenge.
Further development of machine learning methods is needed for improved generalization.
ISLAND offers a promising advancement for computational prediction of protein binding affinity.