Novel Computational and Data-Driven Approaches for Protein Design and Their Application

Dear Colleagues,

Proteins represent a cornerstone of life, being involved in almost all essential processes in the living cell. Hence they have been subjected to a long-term natural optimization to adjust their properties to the requirements of living organisms, i.e., to be highly efficient in their respective tasks under constraints arising from the cellular environment. Such optimization, however, does not usually favor the utilization of proteins outside of their natural niche or for alternative purposes. In fact, there is often a striking disagreement. Therefore, various protein engineering strategies have been developed to tailor the proteins to these new “unnatural” demands. In most cases, the protein engineering amounts to a quite daunting task owing to the enormous sequence space that needs exploring especially when considering multiple-point mutants, and the fact that the frequency with which the beneficial mutation is obtained through random means is a few orders of magnitude lower than that of the deleterious mutation.

Initially, protein engineering was separated to rational and random approaches. Thanks to the impressive development of powerful hardware and efficient algorithms, we have witnessed steady increase in the available and affordable computational resources in the recent decade that brought computational methods to early maturity, enabling their successful application not only to the design of single-point mutations but also to de novo design of whole proteins for a broad range of functions. In parallel, extraordinary progress also commenced in the filed of random methods reaching a notable screening capacity of up to 1012 variants. Despite this advance, solely brute-force approaches are less often employed nowadays, and instead, we see marriages of directed evolutions with computational methods in which computational methods are applied to design suitable smart libraries for directed evolution methods and conversely directed evolution methods are utilized to improve properties of proteins initially devised by in silico approaches. By combining the best of both worlds, approximations introduced into the predictive methods can be alleviated, and the low success rates of random mutagenesis increased notably.

In this view, the primary goal of this Special Issue is to highlight recent thrilling advances in the approaches for the rational protein design and creation of smarter mutant libraries as well as their practical utilization to engineer improved proteins. The issue aims to gather original research articles that describe: (i) development of novel methods, (ii) benchmarking analyses of the existing methods and tools, (iii) generation of mutational datasets suitable for the development and benchmarking of computational methods and tools, (iv) successful applications of computational and/or data-driven approaches that yielded proteins with enhanced properties, thereby documenting the application scope of these methods, and (v) analyses unveiling the reasons for failed designs furthering our molecular-level insight into the protein action, which in turn will fuel the further development of novel engineering methods and strategies. Critical reviews focused on specific and timely topics in rational protein engineering are also invited.

Dr. Jan Brezovsky
Guest Editor

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