For all-atom, explicit solvent, bio-molecular systems of O(100k) atoms you ought now to be using GPU-accelerated codes. Even without knowing exactly the setup of your simulations it is most probable that ACEMD, AMBER, Gromacs, NAMD would all be adequate for your needs.
Generally these codes won't scale beyond a single system for your simulation size (unless with network like Infiniband), or even a few GPUs, and strongly favour GPU performance over CPU, so focus on machine configurations with several high-performance GPUs and good PCIe connectivity. Plan for 1-2 core/GPU. With some codes, there's no need to have multi-CPU systems, since the computation is done on the GPU (note that GROMACS will use both CPU and GPU effectively, so their quality should be balanced), nor employ a high performance interconnect, such as Infiniband.
All the codes use CUDA so Nvidia GPUs are the way to go. Geforce cards are perfectly adequate (eg the 4GB Geforce GTX680), and substantially more economical than the Teslas.
We sell a workstation optimised for ACEMD and other MD codes Acellera Metrocubo. Alternatively, register for the NVidia GPU Test Drive to be put in touch with other suitable hardware resellers.
With regard to the criticism of hydrogen mass re-partitioning, the theoretical and technical basis was first described in:
Improving efficiency of large time-scale molecular dynamics simulations of hydrogen-rich systems Feenstra et al. JCC 1999
It is a widely used method, implemented not only in ACEMD but also Gromacs and, recently, Anton:
Atomic-level description of ubiquitin folding, Piana et al, PNAS (2013)