Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures which limit their applicability to protein complexes with known structures. In this work, we explore sequence based protein binding affinity prediction using machine learning. Specifically, we present our findings of the existing state of the art binding affinity prediction scheme called PPA-Pred2 which claims very high accuracy on a 144-complex benchmark dataset. We have found significant discrepancies between the results reported in the PPA-Pred2 paper and the ones we obtained using their webserver. Our paper highlights the fact that the true generalization performance of even the state of the art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem. We also propose a novel sequence-only predictor of binding affinity called ISLAND which gives better accuracy than PPA-Pred2 webserver and other existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its Python code are available at the URL: URL
Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures which limit their applicability to protein complexes with known structures. In this work, we explore sequence based protein binding affinity prediction using machine learning. Our paper highlights the fact that the generalization performance of even the state of the art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem. We also propose a novel sequence-only predictor of binding affinity called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its Python code are available at the URL: URL
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Specifically, we present our findings of the existing state of the art binding affinity prediction scheme called PPA-Pred2 which claims very high accuracy on a 144-complex benchmark dataset. We have found significant discrepancies between the results reported in the PPA-Pred2 paper and the ones we obtained using their webserver.
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PPA-Pred2 webserver and other