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Updated: Mar 9, 2026
A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Distance-Guided Forward and Backward Chain-Growth Monte Carlo Method for Conformational Sampling and Structural
Ke Tang1, Jinfeng Zhang2, Jie Liang1
1Department of Bioengineering, University of Illinois at Chicago , Chicago, Illinois 60607, United States.
Insights
We developed DiSGro-H3, a novel computational method for predicting antibody H3 loop structures. This approach efficiently generates accurate near-native structures, outperforming existing methods and enabling predictions without templates.
Area of Science:
- Immunology
- Computational Biology
- Structural Biology
Background:
- Antibodies recognize antigens via complementary determining regions (CDRs).
- The H3 loop within CDRs exhibits high sequence and length variability, posing a significant challenge for structural prediction.
- Accurate prediction of H3 loop structures is crucial for understanding antibody diversity and antigen specificity.
Purpose of the Study:
- To develop a novel computational method for predicting the three-dimensional structures of antibody H3 loops.
- To improve the efficiency and accuracy of ab initio H3 loop structure prediction.
- To provide a template-free prediction method for any H3 loop sequence.
Main Methods:
- Developed distance-guided sequential chain-growth Monte Carlo (DiSGro-H3), a novel method for H3 loop structure prediction.
- Employed a chain-growth sequential Monte Carlo approach sampling protein chains in forward and backward directions.
- Utilized predicted conformation types from H3 loop sequences to guide structure generation.
Main Results:
- DiSGro-H3 efficiently generates low-energy, near-native H3 loop structures.
- The method significantly outperforms RosettaAntibody in sampling and prediction accuracy.
- DiSGro-H3 demonstrates comparable performance to template-based methods while offering ab initio capabilities.
- Achieved satisfactory accuracy for H3 loop prediction without relying on templates.
Conclusions:
- DiSGro-H3 represents a significant advancement in ab initio antibody H3 loop structure prediction.
- The method's efficiency and accuracy make it a valuable tool for antibody engineering and design.
- DiSGro-H3 overcomes limitations of existing methods, enabling prediction for diverse H3 loop sequences.
Abstract:
Antibodies recognize antigens through the complementary determining regions (CDR) formed by six-loop hypervariable regions crucial for the diversity of antigen specificities. Among the six CDR loops, the H3 loop is the most challenging to predict because of its much higher variation in sequence length and identity, resulting in much larger and complex structural space, compared to the other five loops. We developed a novel method based on a chain-growth sequential Monte Carlo method, called distance-guided sequential chain-growth Monte Carlo for H3 loops (DiSGro-H3). The new method samples protein chains in both forward and backward directions. It can efficiently generate low energy, near-native H3 loop structures using the conformation types predicted from the sequences of H3 loops. DiSGro-H3 performs significantly better than another ab initio method, RosettaAntibody, in both sampling and prediction, while taking less computational time. It performs comparably to template-based methods. As an ab initio method, DiSGro-H3 offers satisfactory accuracy while being able to predict any H3 loops without templates.
