Statistical validation of RF models

I am currently working on a RandomForest based prediction method using protein sequence data. I have generated two models first model (NF) using standard set of features and the second model (HF) using hybrid features. I have done Mathews Correlation Coefficient (MCC) and Accuracy calculation and the following are my results:

Model 1 (NF): Training Accuracy - 62.85% Testing Accuracy - 56.38 MCC - 0.1673

Model 2 (HF): Training Accuracy - 60.34 Testing Accuracy - 61.78 MCC - 0.1856

Since there is a trade-off in accuracy and MCC between the models am confused about the prediction power of the models. Could you please share your thoughts on which model I should consider for further analysis.