eQuant - A server for fast protein model quality assessment by integrating high-dimensional data and machine learning

Abstract

In molecular biology, reliable protein structure models are essential in order to understand the functional role of proteins as well as diseases related to them. Structures are derived by complex and resource-demanding experiments, whereas in silico structure modeling and refinement approaches are established to cope with experimental limitations. Nevertheless, both experimental and computational methods are prone to errors. In consequence, small local regions or even the whole tertiary structure can be unreliable or erroneous, leading the researcher to formulate false hypotheses and draw false conclusions. Here, we present eQuant, a novel and fast model quality assessment program (MQAP) and server. By utilizing a hybrid approach of established MQAPs in combination with machine learning techniques, eQuant achieves more homogeneous assessments with less uncertainty compared to other established MQAPs. For normal sized protein structures, computation requires less than ten seconds, making eQuant one of the fastest MQAPs available. The eQuant server is freely available at https://biosciences.hs-mittweida.de/equant/.