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We present SimShear: a sim-to-real pipeline for tactile control that allows use of shear information without explicitly modeling shear dynamics in simulation. Shear, which arises from lateral movements across contact surfaces, are critical for tasks involving dynamic object interactions but are challenging to simulate. We introduce shPix2pix: a shear-conditioned U-Net GAN that transforms simulated tactile images absent of shear plus a vector encoding shear information into realistic equivalents that include shear deformations, and show this outperforms baseline pix2pix methods for simulating tactile images and pose/shear prediction. This is applied to two control tasks using a pair of low-cost desktop robotic arms equipped with a vision-based tactile sensor: first, a tactile tracking task, where a follower arm tracks a surface moved by the leader arm; second, a collaborative co-lift task, where both arms jointly hold an object while the leader arm moves along a prescribed trajectory. Our method maintain contact errors within 1-2 mm across varied trajectories where shear sensing is essential for task performance. This work validates the use of sim-to-real shear modeling with rigid-body simulators, opening new possibilities for simulation in tactile robotics.