Computational Advances in Protein Engineering and Enzyme Design

Advances in the synthesis of β-alanine

β-Alanine is the only naturally occurring β-type amino acid in nature, and it is also one of the very promising three-carbon platform compounds that can be applied in cosmetics and food additives and as a precursor in the chemical, pharmaceutical and material fields, with very broad market prospects. β-Alanine can be synthesized through chemical and biological methods. The chemical synthesis method is relatively well developed, but the reaction conditions are extreme, requiring high temperature and pressure and strongly acidic and alkaline conditions; moreover, there are many byproducts that require high energy consumption. Biological methods have the advantages of product specificity, mild conditions, and simple processes, making them more promising production methods for β-alanine. This paper provides a systematic review of the chemical and biological synthesis pathways, synthesis mechanisms, key synthetic enzymes and factors influencing β-alanine, with a view to providing a reference for the development of a highly efficient and green production process for β-alanine and its industrialization, as well as providing a basis for further innovations in the synthesis of β-alanine.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Non-Native Site-Selective Enzyme Catalysis

The ability to site-selectively modify equivalent functional groups in a molecule has the potential to streamline syntheses and increase product yields by lowering step counts. Enzymes catalyze site-selective transformations throughout primary and secondary metabolism, but leveraging this capability for non-native substrates and reactions requires a detailed understanding of the potential and limitations of enzyme catalysis and how these bounds can be extended by protein engineering. In this review, we discuss representative examples of site-selective enzyme catalysis involving functional group manipulation and C-H bond functionalization. We include illustrative examples of native catalysis, but our focus is on cases involving non-native substrates and reactions often using engineered enzymes. We then discuss the use of these enzymes for chemoenzymatic transformations and target-oriented synthesis and conclude with a survey of tools and techniques that could expand the scope of non-native site-selective enzyme catalysis.

The Quantum Chemical Cluster Approach in Biocatalysis

ConspectusThe quantum chemical cluster approach has been used for modeling enzyme active sites and reaction mechanisms for more than two decades. In this methodology, a relatively small part of the enzyme around the active site is selected as a model, and quantum chemical methods, typically density functional theory, are used to calculate energies and other properties. The surrounding enzyme is modeled using implicit solvation and atom fixing techniques. Over the years, a large number of enzyme mechanisms have been solved using this method. The models have gradually become larger as a result of the faster computers, and new kinds of questions have been addressed. In this Account, we review how the cluster approach can be utilized in the field of biocatalysis. Examples from our recent work are chosen to illustrate various aspects of the methodology. The use of the cluster model to explore substrate binding is discussed first. It is emphasized that a comprehensive search is necessary in order to identify the lowest-energy binding mode(s). It is also argued that the best binding mode might not be the productive one, and the full reactions for a number of enzyme-substrate complexes have therefore to be considered to find the lowest-energy reaction pathway. Next, examples are given of how the cluster approach can help in the elucidation of detailed reaction mechanisms of biocatalytically interesting enzymes, and how this knowledge can be exploited to develop enzymes with new functions or to understand the reasons for lack of activity toward non-natural substrates. The enzymes discussed in this context are phenolic acid decarboxylase and metal-dependent decarboxylases from the amidohydrolase superfamily. Next, the application of the cluster approach in the investigation of enzymatic enantioselectivity is discussed. The reaction of strictosidine synthase is selected as a case study, where the cluster calculations could reproduce and rationalize the selectivities of both the natural and non-natural substrates. Finally, we discuss how the cluster approach can be used to guide the rational design of enzyme variants with improved activity and selectivity. Acyl transferase from Mycobacterium smegmatis serves as an instructive example here, for which the calculations could pinpoint the factors controlling the reaction specificity and enantioselectivity. The cases discussed in this Account highlight thus the value of the cluster approach as a tool in biocatalysis. It complements experiments and other computational techniques in this field and provides insights that can be used to understand existing enzymes and to develop new variants with tailored properties.

Redesigning Enzymes for Biocatalysis: Exploiting Structural Understanding for Improved Selectivity

The discovery of new enzymes, alongside the push to make chemical processes more sustainable, has resulted in increased industrial interest in the use of biocatalytic processes to produce high-value and chiral precursor chemicals. Huge strides in protein engineering methodology and in silico tools have facilitated significant progress in the discovery and production of enzymes for biocatalytic processes. However, there are significant gaps in our knowledge of the relationship between enzyme structure and function. This has demonstrated the need for improved computational methods to model mechanisms and understand structure dynamics. Here, we explore efforts to rationally modify enzymes toward changing aspects of their catalyzed chemistry. We highlight examples of enzymes where links between enzyme function and structure have been made, thus enabling rational changes to the enzyme structure to give predictable chemical outcomes. We look at future directions the field could take and the technologies that will enable it.

Engineering and Design of Programmable Genome Editors

Programmable genome editors are enzymes that can be targeted to a specific location in the genome for making site-specific alterations or deletions. The engineering, design, and development of sequence-specific editors has resulted in a dramatic increase in the precision of editing for nucleotide sequences. These editors can target specific locations in a genome, in vivo. The genome editors are being deployed for the development of genetically modified organisms for agriculture and industry, and for gene therapy of inherited human genetic disorders, cancer, and immunotherapy. Experimental and computational studies of structure, binding, activity, dynamics, and folding, reviewed here, have provided valuable insights that have the potential for increasing the functional efficiency of these gene/genome editors. Biochemical and biophysical studies of the specificities of natural and engineered genome editors reveal that increased binding affinity can be detrimental because of the increase of off-target effects and that the engineering and design of genome editors with higher specificity may require modulation and control of the conformational dynamics.