diff --git a/README.md b/README.md
index 667466b..f1ca4e5 100644
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@@ -18,19 +18,19 @@
 
    Here are the main steps which will allow you to build a precise model for CPP design:
    
-     **1. Data curation and cleaning.** All inappropriate or ambiguous data should be removed or corrected.
-     
-     **2. Data unification.** The data presented in Datasets are heterogeneous and should be unified in terms of variables, measurement units etc.
-     
-     **3. System parametriation.** You need to choose the set of parameters to describe CPPs as well as experimental setup. Most of the models use symbolic representations lacking physico-chemical properties crucial for CPP activity prediction.
-     
-     **4. Model selection.** Best-performing models should be choosen for screening depending on the task complexity (sequence classification or sequence generation).
-     
-     **5. Feature selecction.** After model selection, features used in the model should be choosen showing optimal prediction performance, robustness, and interpretability.
-     
-     **6. Evaluation.** Every model should be evaluated beyond performance on train/test datasets. It can be structural analysis of CPP candidates, modelling of interaction with cellular membranes etc.
-     
-     **7. Project design.** All results should be structured and systematized on GitHub for transparency and reproducibility.
+   **1. Data curation and cleaning.** All inappropriate or ambiguous data should be removed or corrected.
+   
+   **2. Data unification.** The data presented in Datasets are heterogeneous and should be unified in terms of variables, measurement units etc.
+   
+   **3. System parametriation.** You need to choose the set of parameters to describe CPPs as well as experimental setup. Most of the models use symbolic representations lacking physico-chemical properties crucial for CPP activity prediction.
+   
+   **4. Model selection.** Best-performing models should be choosen for screening depending on the task complexity (sequence classification or sequence generation).
+   
+   **5. Feature selecction.** After model selection, features used in the model should be choosen showing optimal prediction performance, robustness, and interpretability.
+   
+   **6. Evaluation.** Every model should be evaluated beyond performance on train/test datasets. It can be structural analysis of CPP candidates, modelling of interaction with cellular membranes etc.
+   
+   **7. Project design.** All results should be structured and systematized on GitHub for transparency and reproducibility.
 
 ### Challenges