A facile and efficient transposon mutagenesis method for generation of multi-codon deletions in protein sequences

Enzyme miniaturization: Revolutionizing future biocatalysts

Enzyme miniaturization offers a transformative approach to overcome limitations posed by the large size of conventional enzymes in industrial, therapeutic, and diagnostic applications. However, the evolutionary optimization of enzymes for activity has not inherently favored compact structures, creating challenges for modern applications requiring smaller catalysts. In this review, we surveyed the advantages of miniature enzymes, including enhanced expressivity, folding efficiency, thermostability, and resistance to proteolysis. We described the applications of miniature enzymes as industrial catalysts, therapeutic agents, and diagnostic elements. We highlighted strategies such as genome mining, rational design, random deletion, and de novo design for achieving enzyme miniaturization, integrating both computational and experimental techniques. By investigating these approaches, we aim to provide a framework for advancing enzyme engineering, emphasizing the unique potential of miniature enzymes to revolutionize biocatalysis, gene therapy, and biosensing technologies.

TAWFN: a deep learning framework for protein function prediction

MOTIVATION

Proteins play pivotal roles in biological systems, and precise prediction of their functions is indispensable for practical applications. Despite the surge in protein sequence data facilitated by high-throughput techniques, unraveling the exact functionalities of proteins still demands considerable time and resources. Currently, numerous methods rely on protein sequences for prediction, while methods targeting protein structures are scarce, often employing convolutional neural networks (CNN) or graph convolutional networks (GCNs) individually.

RESULTS

To address these challenges, our approach starts from protein structures and proposes a method that combines CNN and GCN into a unified framework called the two-model adaptive weight fusion network (TAWFN) for protein function prediction. First, amino acid contact maps and sequences are extracted from the protein structure. Then, the sequence is used to generate one-hot encoded features and deep semantic features. These features, along with the constructed graph, are fed into the adaptive graph convolutional networks (AGCN) module and the multi-layer convolutional neural network (MCNN) module as needed, resulting in preliminary classification outcomes. Finally, the preliminary classification results are inputted into the adaptive weight computation network, where adaptive weights are calculated to fuse the initial predictions from both networks, yielding the final prediction result. To evaluate the effectiveness of our method, experiments were conducted on the PDBset and AFset datasets. For molecular function, biological process, and cellular component tasks, TAWFN achieved area under the precision-recall curve (AUPR) values of 0.718, 0.385, and 0.488 respectively, with corresponding Fmax scores of 0.762, 0.628, and 0.693, and Smin scores of 0.326, 0.483, and 0.454. The experimental results demonstrate that TAWFN exhibits promising performance, outperforming existing methods.

AVAILABILITY AND IMPLEMENTATION

The TAWFN source code can be found at: https://github.com/ss0830/TAWFN.

Insertion-Deletion Events Are Depleted in Protein Regions with Predicted Secondary Structure

A fundamental goal in evolutionary biology and population genetics is to understand how selection shapes the fate of new mutations. Here, we test the null hypothesis that insertion-deletion (indel) events in protein-coding regions occur randomly with respect to secondary structures. We identified indels across 11,444 sequence alignments in mouse, rat, human, chimp, and dog genomes and then quantified their overlap with four different types of secondary structure-alpha helices, beta strands, protein bends, and protein turns-predicted by deep-learning methods of AlphaFold2. Indels overlapped secondary structures 54% as much as expected and were especially underrepresented over beta strands, which tend to form internal, stable regions of proteins. In contrast, indels were enriched by 155% over regions without any predicted secondary structures. These skews were stronger in the rodent lineages compared to the primate lineages, consistent with population genetic theory predicting that natural selection will be more efficient in species with larger effective population sizes. Nonsynonymous substitutions were also less common in regions of protein secondary structure, although not as strongly reduced as in indels. In a complementary analysis of thousands of human genomes, we showed that indels overlapping secondary structure segregated at significantly lower frequency than indels outside of secondary structure. Taken together, our study shows that indels are selected against if they overlap secondary structure, presumably because they disrupt the tertiary structure and function of a protein.

A primer to directed evolution: current methodologies and future directions

Directed evolution is one of the most powerful tools for protein engineering and functions by harnessing natural evolution, but on a shorter timescale. It enables the rapid selection of variants of biomolecules with properties that make them more suitable for specific applications. Since the first in vitro evolution experiments performed by Sol Spiegelman in 1967, a wide range of techniques have been developed to tackle the main two steps of directed evolution: genetic diversification (library generation), and isolation of the variants of interest. This review covers the main modern methodologies, discussing the advantages and drawbacks of each, and hence the considerations for designing directed evolution experiments. Furthermore, the most recent developments are discussed, showing how advances in the handling of ever larger library sizes are enabling new research questions to be tackled.

DIMPLE: deep insertion, deletion, and missense mutation libraries for exploring protein variation in evolution, disease, and biology

Insertions and deletions (indels) enable evolution and cause disease. Due to technical challenges, indels are left out of most mutational scans, limiting our understanding of them in disease, biology, and evolution. We develop a low cost and bias method, DIMPLE, for systematically generating deletions, insertions, and missense mutations in genes, which we test on a range of targets, including Kir2.1. We use DIMPLE to study how indels impact potassium channel structure, disease, and evolution. We find deletions are most disruptive overall, beta sheets are most sensitive to indels, and flexible loops are sensitive to deletions yet tolerate insertions.

Insertions and Deletions (Indels): A Missing Piece of the Protein Engineering Jigsaw

Over the years, protein engineers have studied nature and borrowed its tricks to accelerate protein evolution in the test tube. While there have been considerable advances, our ability to generate new proteins in the laboratory is seemingly limited. One explanation for these shortcomings may be that insertions and deletions (indels), which frequently arise in nature, are largely overlooked during protein engineering campaigns. The profound effect of indels on protein structures, by way of drastic backbone alterations, could be perceived as "saltation" events that bring about significant phenotypic changes in a single mutational step. Should we leverage these effects to accelerate protein engineering and gain access to unexplored regions of adaptive landscapes? In this Perspective, we describe the role played by indels in the functional diversification of proteins in nature and discuss their untapped potential for protein engineering, despite their often-destabilizing nature. We hope to spark a renewed interest in indels, emphasizing that their wider study and use may prove insightful and shape the future of protein engineering by unlocking unique functional changes that substitutions alone could never achieve.

A patent-based consideration of latest platforms in the art of directed evolution: a decade long untold story

Directed (or in vitro) evolution of proteins and metabolic pathways requires tools for creating genetic diversity and identifying protein variants with new or improved functional properties. Besides simplicity, reliability, speed, versatility, universal applicability and economy of the technique, the new science of synthetic biology requires improved means for construction of smart and high-quality mutant libraries to better navigate the sequence diversity. In vitro CRISPR/Cas9-mediated mutagenic (ICM) system and machine-learning (ML)-assisted approaches to directed evolution are now in the field to achieve the goal. This review describes the gene diversification strategies, screening and selection methods, in silico (computer-aided), Cas9-mediated and ML-based approaches to mutagenesis, developed especially in the last decade, and their patent position. The objective behind is to emphasize researchers the need for noting which mutagenesis, screening or selection method is patented and then selecting a suitable restriction-free approach to sequence diversity. Techniques and evolved products subject to patent rights need commercial license if their use is for purposes other than private or experimental research.

Insertions and deletions in protein evolution and engineering

Protein evolution or engineering studies are traditionally focused on amino acid substitutions and the way these contribute to fitness. Meanwhile, the insertion and deletion of amino acids is often overlooked, despite being one of the most common sources of genetic variation. Recent methodological advances and successful engineering stories have demonstrated that the time is ripe for greater emphasis on these mutations and their understudied effects. This review highlights the evolutionary importance and biotechnological relevance of insertions and deletions (indels). We provide a comprehensive overview of approaches that can be employed to include indels in random, (semi)-rational or computational protein engineering pipelines. Furthermore, we discuss the tolerance to indels at the structural level, address how domain indels can link the function of unrelated proteins, and feature studies that illustrate the surprising and intriguing potential of frameshift mutations.

Methods for enzyme library creation: Which one will you choose?: A guide for novices and experts to introduce genetic diversity

Enzyme engineering allows to explore sequence diversity in search for new properties. The scientific literature is populated with methods to create enzyme libraries for engineering purposes, however, choosing a suitable method for the creation of mutant libraries can be daunting, in particular for the novices. Here, we address both novices and experts: how can one enter the arena of enzyme library design and what guidelines can advanced users apply to select strategies best suited to their purpose? Section I is dedicated to the novices and presents an overview of established and standard methods for library creation, as well as available commercial solutions. The expert will discover an up-to-date tool to freshen up their repertoire (Section I) and learn of the newest methods that are likely to become a mainstay (Section II). We focus primarily on in vitro methods, presenting the advantages of each method. Our ultimate aim is to offer a selection of methods/strategies that we believe to be most useful to the enzyme engineer, whether a first-timer or a seasoned user.

A novel framework for engineering protein loops exploring length and compositional variation

Insertions and deletions (indels) are known to affect function, biophysical properties and substrate specificity of enzymes, and they play a central role in evolution. Despite such clear significance, this class of mutation remains an underexploited tool in protein engineering with few available platforms capable of systematically generating and analysing libraries of varying sequence composition and length. We present a novel DNA assembly platform (InDel assembly), based on cycles of endonuclease restriction digestion and ligation of standardised dsDNA building blocks, that can generate libraries exploring both composition and sequence length variation. In addition, we developed a framework to analyse the output of selection from InDel-generated libraries, combining next generation sequencing and alignment-free strategies for sequence analysis. We demonstrate the approach by engineering the well-characterized TEM-1 β-lactamase Ω-loop, involved in substrate specificity, identifying multiple novel extended spectrum β-lactamases with loops of modified length and composition-areas of the sequence space not previously explored. Together, the InDel assembly and analysis platforms provide an efficient route to engineer protein loops or linkers where sequence length and composition are both essential functional parameters.

Accessing unexplored regions of sequence space in directed enzyme evolution via insertion/deletion mutagenesis

Insertions and deletions (InDels) are frequently observed in natural protein evolution, yet their potential remains untapped in laboratory evolution. Here we introduce a transposon-based mutagenesis approach (TRIAD) to generate libraries of random variants with short in-frame InDels, and screen TRIAD libraries to evolve a promiscuous arylesterase activity in a phosphotriesterase. The evolution exhibits features that differ from previous point mutagenesis campaigns: while the average activity of TRIAD variants is more compromised, a larger proportion has successfully adapted for the activity. Different functional profiles emerge: (i) both strong and weak trade-off between activities are observed; (ii) trade-off is more severe (20- to 35-fold increased kcat/KM in arylesterase with 60-400-fold decreases in phosphotriesterase activity) and (iii) improvements are present in kcat rather than just in KM, suggesting adaptive solutions. These distinct features make TRIAD an alternative to widely used point mutagenesis, accessing functional innovations and traversing unexplored fitness landscape regions.

Modern methods for laboratory diversification of biomolecules

Genetic variation fuels Darwinian evolution, yet spontaneous mutation rates are maintained at low levels to ensure cellular viability. Low mutation rates preclude the exhaustive exploration of sequence space for protein evolution and genome engineering applications, prompting scientists to develop methods for efficient and targeted diversification of nucleic acid sequences. Directed evolution of biomolecules relies upon the generation of unbiased genetic diversity to discover variants with desirable properties, whereas genome-engineering applications require selective modifications on a genomic scale with minimal off-targets. Here, we review the current toolkit of mutagenesis strategies employed in directed evolution and genome engineering. These state-of-the-art methods enable facile modifications and improvements of single genes, multicomponent pathways, and whole genomes for basic and applied research, while simultaneously paving the way for genome editing therapeutic interventions.

Extensive libraries of gene truncation variants generated by in vitro transposition

The detailed analysis of the impact of deletions on proteins or nucleic acids can reveal important functional regions and lead to variants with improved macromolecular properties. We present a method to generate large libraries of mutants with deletions of varying length that are randomly distributed throughout a given gene. This technique facilitates the identification of crucial sequence regions in nucleic acids or proteins. The approach utilizes in vitro transposition to generate 5΄ and 3΄ fragment sub-libraries of a given gene, which are then randomly recombined to yield a final library comprising both terminal and internal deletions. The method is easy to implement and can generate libraries in three to four days. We used this approach to produce a library of >9000 random deletion mutants of an artificial RNA ligase enzyme representing 32% of all possible deletions. The quality of the library was assessed by next-generation sequencing and detailed bioinformatics analysis. Finally, we subjected this library to in vitro selection and obtained fully functional variants with deletions of up to 18 amino acids of the parental enzyme that had been 95 amino acids in length.