In silico λ-dynamics predicts protein binding specificities to modified RNAs

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Biology

Background:

• RNA modifications are crucial for gene expression, but identifying them is challenging.
• Antibodies are common tools for detecting modified RNA, yet specificity issues can lead to inaccurate results.
• A lack of efficient methods hinders the characterization of numerous RNA modifications.

Approach:

• Utilized in silico λ-dynamics to calculate binding free energy differences between antibodies and various RNA modifications.
• Determined crystal structures of antibodies targeting inosine and N6-methyladenosine (m6A) for structural insights.
• Employed computational predictions to identify RNA modifications that either promote or prevent antibody binding.

Key Points:

• In silico λ-dynamics accurately predicted antibody binding propensities for RNA modifications.
• Experimental in vitro RNA-antibody binding assays validated the computational predictions.
• High agreement between computed and experimental results demonstrates the efficacy of the λ-dynamics approach.

Conclusions:

• λ-dynamics serves as a predictive screen for assessing antibody specificity against diverse RNA modifications.
• This computational strategy offers an innovative method to study RNA modification interactions without experimental limitations.
• The approach facilitates the elucidation of how numerous RNA modifications interact with biological molecules.