A number of grand-challenge scientific applications are unable to harness Terflops-scale computing capabilities of massively-parallel processing (MPP) systems due to their inherent scaling limits. For these applications, multi-paradigm computing systems that provide additional computing capability per processing node using accelerators are a viable solution. Among various generic and custom-designed accelerators that represent a data-parallel programming paradigm, FPGA devices provide a number of performance enhancing features including concurrency, deep-pipelining and streaming in a flexible manner. We demonstrate acceleration of a production-level biomolecular simulation, in which typical speedups are less than 20 on even the most powerful supercomputing systems, on an FPGA-enabled system with a high-level programming interface. Using accurate models of our FPGA implementation and parallel efficiency results obtained on the Cray XT3 system, we project that the time-to-solution is reduced significantly as compared to the microprocessor-only execution times. A further advantage of computing with FPGA-enabled systems over microprocessor-only implementations is performance sustainability for large-scale problems. The computational complexity of a biomolecular simulation is proportional to its problem sizes, hence the runtime on a microprocessor increases at a much faster rate as compared to FPGA-enabled systems which are capable of providing very high throughput for compute-intensive operations thereby sustaining performance for large-scale problems.