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

Area of Science:

• Molecular Biology
• Biochemistry
• Computational Biology

Background:

• RNA modifications are crucial for gene regulation but are difficult to identify.
• Antibodies are used to detect modified RNA, but their specificity can be an issue.
• Limited methods exist to characterize the vast landscape of RNA modifications.

Purpose of the Study:

• To develop and validate a computational method for predicting antibody specificity against RNA modifications.
• To efficiently screen antibody interactions with a wide range of RNA modifications.
• To overcome limitations of experimental methods in characterizing RNA modification-antibody interactions.

Main Methods:

• Utilized in silico λ-dynamics to estimate binding free energy differences between antibodies and modified ribonucleosides.
• Determined crystal structures of antibodies targeting inosine and N6-methyladenosine (m6A).
• Validated computational predictions with in vitro RNA-antibody binding assays.

Main Results:

• In silico λ-dynamics accurately predicted RNA modifications that permit or inhibit antibody binding.
• High agreement was observed between computed and experimental binding propensities.
• Demonstrated the utility of λ-dynamics as a predictive screen for antibody specificity.

Conclusions:

• In silico λ-dynamics is an effective tool for assessing antibody specificity towards RNA modifications.
• This strategy offers an innovative approach to study interactions between biological molecules and numerous RNA modifications.
• The method overcomes limitations of traditional in vitro and in vivo techniques for RNA modification characterization.