Digital illustration of interacting proteins with a highlighted mutation site.

Decoding Protein Interactions: Can AI Predict Mutation Effects?

Reese Marlowe in Science & Nature August 2025 • 4 min read.

"New machine learning model offers a faster, more reliable way to predict how mutations alter protein binding, impacting drug design and personalized medicine."

Proteins are the workhorses of our cells, and their interactions are fundamental to life. When mutations occur in proteins, they can disrupt these interactions, leading to disease. Understanding how mutations affect protein binding is crucial for developing effective therapies, especially for complex conditions like cancer. Traditionally, measuring these effects has been a slow and painstaking process.

Experimental methods for assessing binding affinities, while valuable, are often time-consuming and labor-intensive. This creates a bottleneck, especially with the rapid increase in data on disease-causing mutations generated by modern sequencing technologies. The need for faster, more reliable computational methods to predict the impact of these mutations is greater than ever.

Computational approaches offer a promising alternative, broadly falling into three categories: rigorous methods (like thermodynamic integration), empirical energy-based methods, and machine learning-based methods. Each has its strengths and weaknesses. Machine learning, in particular, is gaining traction for its ability to analyze complex datasets and identify subtle patterns that influence protein interactions.

How iSEE Predicts Mutation Effects

Digital illustration of interacting proteins with a highlighted mutation site.

A new machine learning model called iSEE (interface Structure, Evolution and Energy-based) is changing the game. iSEE uses a combination of structural, evolutionary, and energy-based features to predict how single-point mutations affect protein binding affinity. Unlike some complex models, iSEE achieves high accuracy with a relatively small number of carefully selected features.

The model was trained on a diverse dataset of over 1,100 mutations in 57 different protein-protein complexes. This extensive training allows iSEE to accurately predict changes in binding affinity, as measured by a Pearson correlation coefficient of 0.80 and a root mean square error of 1.41 kcal/mol.

Interface Structure: iSEE considers the 3D structure of the protein complex, focusing on the interface where the proteins interact.
Evolution: The model incorporates evolutionary information, leveraging the fact that conserved amino acids are often critical for protein function and binding.
Energy: iSEE calculates the energy of the interaction, taking into account factors like van der Waals forces and electrostatic interactions.

iSEE's success lies in its ability to balance these different types of information. By considering both the physical structure and the evolutionary history of the proteins, the model can make accurate predictions about the impact of mutations.

Looking Ahead: iSEE's Impact on Protein Research

iSEE represents a significant step forward in our ability to understand and predict the effects of mutations on protein interactions. Its speed, accuracy, and reliance on readily available data make it a valuable tool for researchers in a variety of fields, from drug discovery to personalized medicine. As the model continues to be refined and expanded, it promises to unlock new insights into the complex world of protein interactions and their role in human health.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1002/prot.25630, Alternate LINK

Title: Isee: Interface Structure, Evolution, And Energy-Based Machine Learning Predictor Of Binding Affinity Changes Upon Mutations

Subject: Molecular Biology

Journal: Proteins: Structure, Function, and Bioinformatics

Publisher: Wiley

Authors: Cunliang Geng, Anna Vangone, Gert E. Folkers, Li C. Xue, Alexandre M. J. J. Bonvin

Published: 2018-12-03

Everything You Need To Know

What is iSEE and why is it important?

iSEE is a machine learning model designed to predict how mutations affect protein binding affinity. It uses a combination of structural, evolutionary, and energy-based features to achieve this. The model was trained on a large dataset of mutations and protein complexes, allowing it to make accurate predictions about changes in binding affinity. This is significant because understanding the impact of mutations on protein interactions is crucial for developing effective therapies and advancing personalized medicine.

What kind of information does iSEE use to predict the effects of mutations?

iSEE utilizes three key types of information: interface structure, evolution, and energy. Interface structure considers the 3D arrangement of the protein complex at the interaction site. Evolutionary information leverages the fact that conserved amino acids are often critical for protein function and binding. Energy calculations take into account factors like van der Waals forces and electrostatic interactions. By balancing these aspects, iSEE can accurately predict the impact of mutations.

How does iSEE compare to traditional methods of assessing protein binding affinities?

Computational approaches, including rigorous methods, empirical energy-based methods, and machine learning-based methods, offer a faster and more reliable alternative to experimental methods. iSEE falls into the machine learning category, which is gaining popularity due to its ability to analyze complex datasets and identify subtle patterns that influence protein interactions. iSEE's predictive capabilities can significantly accelerate research in drug discovery and personalized medicine.

How accurate is iSEE in predicting the effects of mutations on protein binding?

The accuracy of iSEE is measured by a Pearson correlation coefficient of 0.80 and a root mean square error of 1.41 kcal/mol. This level of accuracy is significant because it allows researchers to confidently use iSEE's predictions to guide their experiments and drug development efforts. The model's ability to accurately predict changes in binding affinity makes it a valuable tool for understanding the impact of mutations on protein interactions.

How can iSEE be used in drug discovery and personalized medicine?

iSEE can significantly impact drug discovery and personalized medicine by providing a faster, more reliable way to predict how mutations affect protein binding. This is crucial for developing effective therapies, especially for complex conditions like cancer. By understanding how mutations disrupt protein interactions, researchers can design drugs that specifically target these interactions and improve patient outcomes. Additionally, iSEE can help identify individuals who are more likely to respond to certain treatments based on their unique genetic makeup.