Computational approaches for rational design of proteins with novel functionalities

Rational engineering S1' substrate binding pocket to enhance substrate specificity and catalytic activity of thermal-stable keratinase for efficient keratin degradation

This study reports the rational engineering of the S1' substrate-binding pocket of a thermally-stable keratinase from Pseudomonas aeruginosa 4-3 (4-3Ker) to improve substrate specificity to typical keratinase (K/C > 0.5) and catalytic activity without compromising thermal stability for efficient keratin degradation. Of 10 chosen mutation hotspots in the S1' substrate-binding pocket, the top three mutations M128R, A138V, and V142I showing the best catalytic activity and substrate specificity were identified. Their double and triple combinatorial mutants synergistically overcame limitations of single mutants, fabricating an excellent M128R/A138V/V142I triple mutant which displayed a 1.21-fold increase in keratin catalytic activity, 1.10-fold enhancement in keratin/casein activity ratio, and a 3.13 °C increase in half-inactivation temperature compared to 4-3Ker. Molecular dynamics simulations revealed enhanced flexibility of critical amino acid residues at the substrate access tunnel, improved global protein rigidity, and heightened hydrophobicity within the active site likely underpinned the increased catalytic activity and substrate specificity. Additionally, the triple mutant improved the feather degradation rate by 32.86 % over the wild-type, far exceeding commercial keratinase in substrate specificity and thermal stability. This study exemplified engineering a typical keratinase with enhanced substrate specificity, catalytic activity, and thermal stability from thermally-stable 4-3Ker, providing a more robust tool for feather degradation.

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".

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Rational design of mini protein mimicking uricase: Encapsulation in ZIF-8 for uric acid detection

Five mini proteins mimicking uricase comprising 20, 40, 60, 80, and 100 amino acids were designed based on the conserved active site residues within the same dimer, using the crystal structure of tetrameric uricase from Arthrobacter globiformis (PDB ID: 2yzb) as a template. Based on molecular docking analysis, the smallest mini protein, mp20, shared similar residues to that of native uricase that formed hydrogen bonds with uric acid and was chosen for further studies. Although purified recombinant mp20 did not exhibit uricase activity, it showed specific binding towards uric acid and evinced excellent thermotolerance and structural stability at temperatures ranging from 10°C to 100°C, emulating its natural origin. To explore the potential of mp20 as a bioreceptor in uric acid sensing, mp20 was encapsulated within zeolitic imidazolate framework-8 (mp20@ZIF-8) followed by the modification on rGO-screen printed electrode (rGO/SPCE) to maintain the structural stability. An irreversible anodic peak and increased semicircular arcs of the Nyquist plot with an increase of the analyte concentrations were observed by utilizing cyclic voltammetry and electrochemical impedance spectroscopy (EIS), suggesting the detection of uric acid occurred, which is based on substrate-mp20 interaction.

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Food enzymes have an important role in the improvement of certain food characteristics, such as texture improvement, elimination of toxins and allergens, production of carbohydrates, enhancing flavor/appearance characteristics. Recently, along with the development of artificial meats, food enzymes have been employed to achieve more diverse functions, especially in converting non-edible biomass to delicious foods. Reported food enzyme modifications for specific applications have highlighted the significance of enzyme engineering. However, using direct evolution or rational design showed inherent limitations due to the mutation rates, which made it difficult to satisfy the stability or specific activity needs for certain applications. Generating functional enzymes using de novo design, which highly assembles naturally existing enzymes, provides potential solutions for screening desired enzymes. Here, we describe the functions and applications of food enzymes to introduce the need for food enzymes engineering. To illustrate the possibilities of using de novo design for generating diverse functional proteins, we reviewed protein modelling and de novo design methods and their implementations. The future directions for adding structural data for de novo design model training, acquiring diversified training data, and investigating the relationship between enzyme-substrate binding and activity were highlighted as challenges to overcome for the de novo design of food enzymes.

Application of Af4-Multidetection to Liraglutide in Its Formulation: Preserving and Representing Native Aggregation

Aggregation is among the most critical parameters affecting the pharmacological and safety profile of peptide Active Pharmaceutical Ingredients (APIs). For this reason, it is of utmost importance to define the exact aggregation state of peptide drugs, particularly when the API is marketed as a ready-to-use solution. Consequently, appropriate non-destructive techniques able to replicate the peptide environment must be employed. In our work, we exploited Asymmetrical Flow Field-Flow Fractionation (AF4), connected to UV, dRI, fluorescence, and MALS detectors, to fully characterize the aggregation state of Liraglutide, a peptide API used for the treatment of diabetes type 2 and chronic obesity. In previous studies, Liraglutide was hypothesized to assemble into hexa-octamers in phosphate buffer, but no information on its behavior in the formulation medium was provided up to now. The method used allowed researchers to work using formulation as the mobile phase with excellent recoveries and LoQ/LoD, discerning between stable and degraded samples, and detecting, when present, aggregates up to 108 Da. The native state of Liraglutide was assessed and found to be an association into pentamers, with a non-spherical conformation. Combined to benchmark analyses, the sameness study was complete and descriptive, also giving insight on the aggregation process and covalent/non-covalent aggregate types.

Switching promotor recognition of phage RNA polymerase in silico along lab-directed evolution path

In this work, we computationally investigated how a viral RNA polymerase (RNAP) from bacteriophage T7 evolves into RNAP variants under lab-directed evolution to switch recognition from T7 promoter to T3 promoter in transcription initiation. We first constructed a closed initiation complex for the wild-type T7 RNAP and then for six mutant RNAPs discovered from phage-assisted continuous evolution experiments. All-atom molecular dynamics simulations up to 1 μs each were conducted on these RNAPs in a complex with the T7 and T3 promoters. Our simulations show notably that protein-DNA electrostatic interactions or stabilities at the RNAP-DNA promoter interface well dictate the promoter recognition preference of the RNAP and variants. Key residues and structural elements that contribute significantly to switching the promoter recognition were identified. Followed by a first point mutation N748D on the specificity loop to slightly disengage the RNAP from the promoter to hinder the original recognition, we found an auxiliary helix (206-225) that takes over switching the promoter recognition upon further mutations (E222K and E207K) by forming additional charge interactions with the promoter DNA and reorientating differently on the T7 and T3 promoters. Further mutations on the AT-rich loop and the specificity loop can fully switch the RNAP-promoter recognition to the T3 promoter. Overall, our studies reveal energetics and structural dynamics details along an exemplary directed evolutionary path of the phage RNAP variants for a rewired promoter recognition function. The findings demonstrate underlying physical mechanisms and are expected to assist knowledge and data learning or rational redesign of the protein enzyme structure function.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

New approaches for the characterization of plastic-associated microbial communities and the discovery of plastic-degrading microorganisms and enzymes

Plastics in the environment represent new substrates for microbial colonization, and recent methodological advances allow for in-depth characterization of plastic-associated microbial communities (PAMCs). Over the past several decades, discovery of plastic degrading enzymes (PDEs) and plastic degrading microorganisms (PDMs) has been driven by efforts to understand microbially-mediated plastic degradation in the environment and to discover biocatalysts for plastic processing. In this review, we discuss the evolution of methodology in plastic microbiology and highlight major advancements in the field stemming from computational microbiology. Initial research relied largely on culture-based approaches like clear-zone assays to screen for PDMs and microscopy to characterize PAMCs. New computational tools and sequencing technologies are accelerating discoveries in the field through culture-independent and multi-omic approaches, rapidly generating targets for protein engineering and improving the potential for plastic-waste management.

Thermostable cellulose saccharifying microbial enzymes: Characteristics, recent advances and biotechnological applications

Cellulases play a promising role in the bioconversion of renewable lignocellulosic biomass into fermentable sugars which are subsequently fermented to biofuels and other value-added chemicals. Besides biofuel industries, they are also in huge demand in textile, detergent, and paper and pulp industries. Low titres of cellulase production and processing are the main issues that contribute to high enzyme cost. The success of ethanol-based biorefinery depends on high production titres and the catalytic efficiency of cellulases functional at elevated temperatures with acid/alkali tolerance and the low cost. In view of their wider application in various industrial processes, stable cellulases that are active at elevated temperatures in the acidic-alkaline pH ranges, and organic solvents and salt tolerance would be useful. This review provides a recent update on the advances made in thermostable cellulases. Developments in their sources, characteristics and mechanisms are updated. Various methods such as rational design, directed evolution, synthetic & system biology and immobilization techniques adopted in evolving cellulases with ameliorated thermostability and characteristics are also discussed. The wide range of applications of thermostable cellulases in various industrial sectors is described.

Different strategies for the lipase immobilization on the chitosan based supports and their applications

Immobilized enzymes have received incredible interests in industry, pharmaceuticals, chemistry and biochemistry sectors due to their various advantages such as ease of separation, multiple reusability, non-toxicity, biocompatibility, high activity and resistant to environmental changes. This review in between various immobilized enzymes focuses on lipase as one of the most practical enzyme and chitosan as a preferred biosupport for lipase immobilization and provides a broad range of studies of recent decade. We highlight several aspects of lipase immobilization on the surface of chitosan support containing various types of lipase and immobilization techniques from physical adsorption to covalent bonding and cross-linking with their benefits and drawbacks. The recent advances and future perspectives that can improve the present problems with lipase and chitosan such as high-price of lipase and low mechanical resistance of chitosan are also discussed. According to the literature, optimization of immobilization methods, combination of these methods with other techniques, physical and chemical modifications of chitosan, co-immobilization and protein engineering can be useful as a solution to overcome the mentioned limitations.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

A comprehensive binding study illustrates ligand recognition in the periplasmic binding protein PotF

Periplasmic binding proteins (PBPs) are ubiquitous receptors in gram-negative bacteria. They sense solutes and play key roles in nutrient uptake. Escherichia coli's putrescine receptor PotF has been reported to bind putrescine and spermidine. We reveal that several similar biogenic polyamines are recognized by PotF. Using isothermal titration calorimetry paired with X-ray crystallography of the different complexes, we unveil PotF's binding modes in detail. The binding site for PBPs is located between two lobes that undergo a large conformational change upon ligand recognition. Hence, analyzing the influence of ligands on complex formation is crucial. Therefore, we solved crystal structures of an open and closed apo state and used them as a basis for molecular dynamics simulations. In addition, we accessed structural behavior in solution for all complexes by ¹H-¹⁵N HSQC NMR spectroscopy. This combined analysis provides a robust framework for understanding ligand binding for future developments in drug design and protein engineering.

Advances in Recombinant Lipases: Production, Engineering, Immobilization and Application in the Pharmaceutical Industry

Lipases are one of the most used enzymes in the pharmaceutical industry due to their efficiency in organic syntheses, mainly in the production of enantiopure drugs. From an industrial viewpoint, the selection of an efficient expression system and host for recombinant lipase production is highly important. The most used hosts are Escherichia coli and Komagataella phaffii (previously known as Pichia pastoris) and less often reported Bacillus and Aspergillus strains. The use of efficient expression systems to overproduce homologous or heterologous lipases often require the use of strong promoters and the co-expression of chaperones. Protein engineering techniques, including rational design and directed evolution, are the most reported strategies for improving lipase characteristics. Additionally, lipases can be immobilized in different supports that enable improved properties and enzyme reuse. Here, we review approaches for strain and protein engineering, immobilization and the application of lipases in the pharmaceutical industry.

Properties and Applications of Extremozymes from Deep-Sea Extremophilic Microorganisms: A Mini Review

The deep sea, which is defined as sea water below a depth of 1000 m, is one of the largest biomes on the Earth, and is recognised as an extreme environment due to its range of challenging physical parameters, such as pressure, salinity, temperature, chemicals and metals (such as hydrogen sulphide, copper and arsenic). For surviving in such extreme conditions, deep-sea extremophilic microorganisms employ a variety of adaptive strategies, such as the production of extremozymes, which exhibit outstanding thermal or cold adaptability, salt tolerance and/or pressure tolerance. Owing to their great stability, deep-sea extremozymes have numerous potential applications in a wide range of industries, such as the agricultural, food, chemical, pharmaceutical and biotechnological sectors. This enormous economic potential combined with recent advances in sampling and molecular and omics technologies has led to the emergence of research regarding deep-sea extremozymes and their primary applications in recent decades. In the present review, we introduced recent advances in research regarding deep-sea extremophiles and the enzymes they produce and discussed their potential industrial applications, with special emphasis on thermophilic, psychrophilic, halophilic and piezophilic enzymes.

Recent Advancements in Non-Invasive Formulations for Protein Drug Delivery

Advancements in biotechnology and protein engineering expand the availability of various therapeutic proteins including vaccines, antibodies, hormones, and growth factors. In addition, protein drugs hold many therapeutic advantages over small synthetic drugs in terms of high specificity and activity. This has led to further R&D investment in protein-based drug products and an increased number of drug approvals for therapeutic proteins. However, there are many biological and biopharmaceutical obstacles inherent to protein drugs including physicochemical and enzymatic destabilization, which limit their development and clinical application. Therefore, effective formulations of therapeutic proteins are needed to overcome the various physicochemical and biological barriers. In current medical practice, protein drugs are predominantly available in injectable formulations, which have disadvantages including pain, the possibility of infection, high cost, and low patient compliance. Consequently, non-invasive drug delivery systems for therapeutic proteins have gained great attention in the research and development of biomedicines. Therefore, this review covers the various formulation approaches to optimizing the delivery properties of protein drugs with an emphasis on improving bioavailability and patient compliance. It provides a comprehensive update on recent advancements in nanotechnologies with regard to non-invasive protein drug delivery systems, which is also categorized by the route of administrations including oral, nasal, transdermal, pulmonary, ocular, and rectal delivery systems.

Genetically Engineered Proteins to Improve Biomass Conversion: New Advances and Challenges for Tailoring Biocatalysts

Protein engineering emerged as a powerful approach to generate more robust and efficient biocatalysts for bio-based economy applications, an alternative to ecologically toxic chemistries that rely on petroleum. On the quest for environmentally friendly technologies, sustainable and low-cost resources such as lignocellulosic plant-derived biomass are being used for the production of biofuels and fine chemicals. Since most of the enzymes used in the biorefinery industry act in suboptimal conditions, modification of their catalytic properties through protein rational design and in vitro evolution techniques allows the improvement of enzymatic parameters such as specificity, activity, efficiency, secretability, and stability, leading to better yields in the production lines. This review focuses on the current application of protein engineering techniques for improving the catalytic performance of enzymes used to break down lignocellulosic polymers. We discuss the use of both classical and modern methods reported in the literature in the last five years that allowed the boosting of biocatalysts for biomass degradation.

Protein-based parts and devices that respond to intracellular and extracellular signals in mammalian cells

Synthetic biology aims to rewire cellular activities and functionality by implementing genetic circuits with high biocomputing capabilities. Recent efforts led to the development of smart sensing interfaces which integrate multiple inputs to activate desired outputs in a highly specific and sensitive manner. In this review, we highlight protein-based interfaces that sense intracellular or extracellular cues providing information about dynamic environmental changes and cellular state. We will also discuss different mechanisms of regulation of gene expression connected to the sensors to develop diagnostic and therapeutic devices. We conclude discussing challenges and opportunities for biomedical applications of synthetic mammalian protein-based devices.

Engineering of lysine cyclodeaminase conformational dynamics for relieving substrate and product inhibitions in the biosynthesis of l-pipecolic acid

Efficient biocatalytic process construction by relieving substrate and product inhibitions via identification and engineering of enzyme conformational dynamics.

Designing the Sniper: Improving Targeted Human Cytolytic Fusion Proteins for Anti-Cancer Therapy via Molecular Simulation

Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to upregulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such "high-resolution" detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields.

CADEE: Computer-Aided Directed Evolution of Enzymes

The tremendous interest in enzymes as biocatalysts has led to extensive work in enzyme engineering, as well as associated methodology development. Here, a new framework for computer-aided directed evolution of enzymes (CADEE) is presented which allows a drastic reduction in the time necessary to prepare and analyze in silico semi-automated directed evolution of enzymes. A pedagogical example of the application of CADEE to a real biological system is also presented in order to illustrate the CADEE workflow.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

A new concept for molecular engineering of artificial enzymes: a multiscale simulation

We propose a new concept for the design of artificial enzymes from synthetic protein-like copolymers and non-natural functional monomers which in terms of their affinity for water can be divided into two categories: hydrophobic and hydrophilic. Hydrophilic monomers comprise catalytically active groups similar to those in the corresponding amino acid residues. A key ingredient of our approach is that the target globular conformation of protein-like, core-shell morphology with multiple catalytic groups appears spontaneously in the course of controlled radical polymerization in a selective solvent. As a proof of concept, we construct a fully synthetic analog of serine hydrolase, e.g.α-chymotrypsin, using the conformation-dependent sequence design approach and multiscale simulation that combines the methods of "mesoscale chemistry" and atomistic molecular dynamics (MD). A 100 ns GPU-accelerated MD simulation of the designed polymer-supported catalyst in the aqueous environment provides valuable information on the structural organization of this system that has been synthesized in our Lab.

Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework

Agent-based modelling is being used to represent biological systems with increasing frequency and success. This paper presents the implementation of a new tool for biomolecular reaction modelling in the open source Multiagent Simulator of Neighborhoods framework. The rationale behind this new tool is the necessity to describe interactions at the molecular level to be able to grasp emergent and meaningful biological behaviour. We are particularly interested in characterising and quantifying the various effects that facilitate biocatalysis. Enzymes may display high specificity for their substrates and this information is crucial to the engineering and optimisation of bioprocesses. Simulation results demonstrate that molecule distributions, reaction rate parameters, and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of realistic cell environments. While higher percentage of collisions with occurrence of reaction increases the affinity of the enzyme to the substrate, a faster reaction (i.e., turnover number) leads to a smaller number of time steps. Slower diffusion rates and molecular crowding (physical hurdles) decrease the collision rate of reactants, hence reducing the reaction rate, as expected. Also, the random distribution of molecules affects the results significantly.

Computational protein design methods for synthetic biology

Computational protein design, a process that searches for mutants with desired improved properties, plays a central role in the conception of many synthetic biology devices including biosensors, bioproduction, or regulation circuits. To that end, a rational workflow for computational protein design is described here consisting of (a) searching in the sequence, structure or chemical spaces for the desired function and associated protein templates; (b) finding the list of potential hot regions to mutate in the parent proteins; and (c) performing in silico screening of mutants with predicted improved properties.

A frequency-based linguistic approach to protein decoding and design: Simple concepts, diverse applications, and the SCS Package

Protein structure and function information is coded in amino acid sequences. However, the relationship between primary sequences and three-dimensional structures and functions remains enigmatic. Our approach to this fundamental biochemistry problem is based on the frequencies of short constituent sequences (SCSs) or words. A protein amino acid sequence is considered analogous to an English sentence, where SCSs are equivalent to words. Availability scores, which are defined as real SCS frequencies in the non-redundant amino acid database relative to their probabilistically expected frequencies, demonstrate the biological usage bias of SCSs. As a result, this frequency-based linguistic approach is expected to have diverse applications, such as secondary structure specifications by structure-specific SCSs and immunological adjuvants with rare or non-existent SCSs. Linguistic similarities (e.g., wide ranges of scale-free distributions) and dissimilarities (e.g., behaviors of low-rank samples) between proteins and the natural English language have been revealed in the rank-frequency relationships of SCSs or words. We have developed a web server, the SCS Package, which contains five applications for analyzing protein sequences based on the linguistic concept. These tools have the potential to assist researchers in deciphering structurally and functionally important protein sites, species-specific sequences, and functional relationships between SCSs. The SCS Package also provides researchers with a tool to construct amino acid sequences de novo based on the idiomatic usage of SCSs.