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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Rational protein engineering
• Engineering protein stability
• Engineering protein function
• Computational methods and tools
• Molecular modeling of proteins
• Protein bioinformatics
• De novo protein design
• Artificial metalloenzymes
• Machine-learning
• Interfering effects of mutations
• Structure-function relationships in proteins
• Sequence-function relationships in proteins
• Deep mutational scanning
• Broad mutational scanning