Motivation: RNA structure plays a central role in how transcripts function, but inferring it reliably remains difficult, especially when pseudoknots need to be part of the prediction. Chemical probing experiments provide additional signals, yet these signals do not directly identify base pairing partners. RNA proximity ligation provides direct evidence of base pairing, but balancing this evidence with pseudoknot prediction accuracy and scalability of structure prediction for long sequences remains challenging. Results: We present CPLfold, a fast and flexible RNA folding method that combines thermodynamic modeling with chimeric evidence from RNA cross-linking and ligation experiments, while naturally supporting pseudoknots. CPLfold scales to long sequences and recovers more accurate global structures and long-range interactions than existing approaches across multiple benchmarks such as COMRADES and IRIS. By tuning two simple trade-off parameters (, {beta}) the method allows flexibility in the level of incorporating chimeric evidence and asserting pseudoknots. Availability and Implementation: Source code and scripts are available at https://github.com/Vicky-0256/CPLfold.