The paper introduces AFsample2, a method that enhances the prediction of multiple conformations and ensembles for proteins using AlphaFold2 (AF2). AFsample2 employs random MSA column masking to reduce the influence of co-evolutionary signals, thereby increasing the structural diversity of models generated by AF2. The method improves the prediction of alternative states for a broad range of proteins, yielding high-quality end states and diverse conformational ensembles. In the open-closed conformation dataset (OC23), alternate state models improved in 17 out of 23 cases without compromising the generation of the preferred state. Consistent results were observed in 16 membrane protein transporters, with improvements in 12 out of 16 targets. TM-scores to experimental end states improved substantially, sometimes exceeding 50%, elevating mediocre scores from 0.58 to nearly perfect 0.98. AFsample2 also increased the diversity of intermediate conformations by 70% compared to the standard AF2 system. The paper also proposes a novel strategy to select end-states in generated model ensembles. These improvements enhance the generation and identification of alternative protein conformations, providing a more comprehensive understanding of protein function and dynamics. Future work will focus on validating the accuracy of these intermediate conformations and exploring their relevance to functional transitions in proteins.The paper introduces AFsample2, a method that enhances the prediction of multiple conformations and ensembles for proteins using AlphaFold2 (AF2). AFsample2 employs random MSA column masking to reduce the influence of co-evolutionary signals, thereby increasing the structural diversity of models generated by AF2. The method improves the prediction of alternative states for a broad range of proteins, yielding high-quality end states and diverse conformational ensembles. In the open-closed conformation dataset (OC23), alternate state models improved in 17 out of 23 cases without compromising the generation of the preferred state. Consistent results were observed in 16 membrane protein transporters, with improvements in 12 out of 16 targets. TM-scores to experimental end states improved substantially, sometimes exceeding 50%, elevating mediocre scores from 0.58 to nearly perfect 0.98. AFsample2 also increased the diversity of intermediate conformations by 70% compared to the standard AF2 system. The paper also proposes a novel strategy to select end-states in generated model ensembles. These improvements enhance the generation and identification of alternative protein conformations, providing a more comprehensive understanding of protein function and dynamics. Future work will focus on validating the accuracy of these intermediate conformations and exploring their relevance to functional transitions in proteins.