OBJECTIVES/GOALS: This report evaluates participants’experiences from three universities who assembled a complex grant proposal related to research on post-acute sequala of COVID-19 (PASC), also called long COVID. Activities reviewed ranged from the assembly of the team to responses to reviews by the National Center for Advancing Translational Sciences (NCATS). METHODS/STUDY POPULATION: Data were collected by means of semi-structured interviews, conducted and recorded on Zoom, with a sample of 15 scientists and staff both during proposal assembly and following proposal review. The sample comprised 40% of the total team equally selected from the 3 universities, The interview protocol was reviewed by the IRB at UTMB and the interviews were recorded on Zoom, and analyzed by means of the constant comparative strategy in the grounded theory method of qualitative research. Given the relatively small number of interviews in this project, we paid special attention to preserving the confidentiality of respondents. Only the verbal tracks of the interviews were professionally transcribed. Respondents were asked to suggest changes for future inter-organizational proposals. RESULTS/ANTICIPATED RESULTS: FIRST INTERVIEWS *LEADERSHIP: The scope of leadership opportunities was expanded as sub-teams in specific areas such as community engagement were formed. *TEAM: Each university’s community engagement team specializes in a different ethnic clientele, precluding a singular statement for the proposal. SECOND INTERVIEWS *LEADERSHIP: Staff members noted that the team concept too easily evolved into a bureaucratic format, resulting in less negotiation and more direction. *ASSEMBLY TASKS: The Writing Team turned out to be one of the most critical staff teams. *COMMUNICATION: The behavioral scientists in community engagement do not necessarily share paradigms (e.g., public health, psychology, and social work). They had difficulty generating productive communication and a unified statement for the proposal. DISCUSSION/SIGNIFICANCE: The scientists, as a group, suggested that future proposals should focus on one general topic, such as the microbiome, as opposed to attempting to integrate widely divergent interests. The scientists as a group should decide a priori whether to treat innovative ideas such as machine learning science as a science or a service.