This paper evaluates the performance of various methods for predicting transmembrane regions (MSRs) in proteins. The study compares the accuracy of different computational tools in predicting the topology of transmembrane proteins, which are essential for cellular functions such as signaling, ion transport, and drug resistance. The evaluation is based on a set of 188 well-characterized proteins with 883 known MSRs, determined either from their structures or through fusion experiments. The results show that TMHMM is currently the best performing transmembrane prediction program. It correctly predicts a high percentage of MSRs and is particularly good at distinguishing between soluble and transmembrane proteins. MEMSAT is the second best method, although it produces more errors than TMHMM. Other methods, such as ALOM2, PHD, TMAP, Toppred2, and DAS, have varying levels of accuracy, with some methods showing a tendency to over-predict. The study also evaluates the performance of methods on proteins not used for training, showing that TMHMM still performs well. However, some proteins, especially those with multiple transmembrane regions, are more challenging to predict. The results indicate that Hidden Markov Models (HMMs) outperform sliding window approaches in difficult cases. The evaluation also includes a test on G-protein coupled receptors (GPCRs), which are known to have seven transmembrane regions. TMHMM and HMMTOP performed well in this test, while other methods had lower accuracy. The study emphasizes the importance of using signal sequence prediction methods in conjunction with transmembrane prediction tools to ensure accurate results. Overall, the study concludes that TMHMM is the best method for predicting transmembrane regions, followed by MEMSAT. The results highlight the need for further improvements in prediction methods, particularly in handling complex cases and reducing false positives. The study also suggests that integrating multiple prediction methods could improve the accuracy of transmembrane region predictions.This paper evaluates the performance of various methods for predicting transmembrane regions (MSRs) in proteins. The study compares the accuracy of different computational tools in predicting the topology of transmembrane proteins, which are essential for cellular functions such as signaling, ion transport, and drug resistance. The evaluation is based on a set of 188 well-characterized proteins with 883 known MSRs, determined either from their structures or through fusion experiments. The results show that TMHMM is currently the best performing transmembrane prediction program. It correctly predicts a high percentage of MSRs and is particularly good at distinguishing between soluble and transmembrane proteins. MEMSAT is the second best method, although it produces more errors than TMHMM. Other methods, such as ALOM2, PHD, TMAP, Toppred2, and DAS, have varying levels of accuracy, with some methods showing a tendency to over-predict. The study also evaluates the performance of methods on proteins not used for training, showing that TMHMM still performs well. However, some proteins, especially those with multiple transmembrane regions, are more challenging to predict. The results indicate that Hidden Markov Models (HMMs) outperform sliding window approaches in difficult cases. The evaluation also includes a test on G-protein coupled receptors (GPCRs), which are known to have seven transmembrane regions. TMHMM and HMMTOP performed well in this test, while other methods had lower accuracy. The study emphasizes the importance of using signal sequence prediction methods in conjunction with transmembrane prediction tools to ensure accurate results. Overall, the study concludes that TMHMM is the best method for predicting transmembrane regions, followed by MEMSAT. The results highlight the need for further improvements in prediction methods, particularly in handling complex cases and reducing false positives. The study also suggests that integrating multiple prediction methods could improve the accuracy of transmembrane region predictions.