In silico methods for design of biological therapeutics

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives

INTRODUCTION

The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being.

AREAS COVERED

This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies.

EXPERT OPINION

The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

Targeting Peptides: The New Generation of Targeted Drug Delivery Systems

Peptides can act as targeting molecules, analogously to oligonucleotide aptamers and antibodies. They are particularly efficient in terms of production and stability in physiological environments; in recent years, they have been increasingly studied as targeting agents for several diseases, from tumors to central nervous system disorders, also thanks to the ability of some of them to cross the blood-brain barrier. In this review, we will describe the techniques employed for their experimental and in silico design, as well as their possible applications. We will also discuss advancements in their formulation and chemical modifications that make them even more stable and effective. Finally, we will discuss how their use could effectively help to overcome various physiological problems and improve existing treatments.

In Silico Approach of Glycinin and Conglycinin Chains of Soybean By-Product (Okara) Using Papain and Bromelain

This study explores utilization of a sustainable soybean by-product (okara) based on in silico approach. In silico approaches, as well as the BIOPEP database, PeptideRanker database, Peptide Calculator database (Pepcalc), ToxinPred database, and AllerTop database, were employed to evaluate the potential of glycinin and conglycinin derived peptides as a potential source of bioactive peptides. These major protein precursors have been found as protein in okara as a soybean by-product. Furthermore, primary structure, biological potential, and physicochemical, sensory, and allergenic characteristics of the theoretically released antioxidant peptides were predicted in this research. Glycinin and α subunits of β-conglycinin were selected as potential precursors of bioactive peptides based on in silico analysis. The most notable among these are antioxidant peptides. First, the potential of protein precursors for releasing bioactive peptides was evaluated by determining the frequency of occurrence of fragments with a given activity. Through the BIOPEP database analysis, there are several antioxidant bioactive peptides in glycinin and β and α subunits of β-conglycinin sequences. Then, an in silico proteolysis using selected enzymes (papain, bromelain) to obtain antioxidant peptides was investigated and then analyzed using PeptideRanker and Pepcalc. Allergenic analysis using the AllerTop revealed that all in silico proteolysis-derived antioxidant peptides are probably nonallergenic peptides. We also performed molecular docking against MPO (myeloperoxidases) for this peptide. Overall, the present study highlights that glycinin and β and α subunits of β-conglycinin could be promising precursors of bioactive peptides that have an antioxidant peptide for developing several applications.

Novel serratiopeptidase exhibits different affinities to the substrates and inhibitors

The clinical application of serratiopeptidase as an anti-biofilm and anti-inflammatory agent, is restricted due to the enzyme sensitivity to the environmental conditions. In our previous study, six enzyme variants were designed by introducing different mutations and truncations that exhibited higher thermal stability. In the present study, the interaction pattern and affinity of variants to substrates and inhibitors were studied using molecular docking and in-vitro studies. CABS-dock and Swiss-dock servers were used for substrate (Bradykinin and Substance-P) and inhibitor (Lisinopril and EDTA) docking, respectively. The interactions were analyzed using LigPlot, UCSF Chimera, and VMD packages. Free energy calculations were performed using PRODIGY. Finally, the native enzyme and the best variant in terms of interaction pattern and binding score were selected for in-vitro affinity analysis towards Bradykinin and EDTA using HPLC and casein hydrolysis test, respectively. Molecular docking revealed that T344 [8-339ss] variant showed a different pattern for both substrates and inhibitors in the way that none of the native active site residues were involved in the receptor binding. As revealed by in-vitro studies, T344 [8-339ss] displayed the highest number of hydrogen bond formation in docking with Bradykinin and remarkable decrement in the binding affinity for EDTA. This was the first report on the design of novel serratiopeptidase with higher activity to Bradykinin and improved resistance to EDTA as an inhibitor.

Advances in computational structure-based antibody design

Antibodies are currently the most important class of biotherapeutics and are used to treat numerous diseases. Recent advances in computational methods are ushering in a new era of antibody design, driven in part by accurate structure prediction. Previously, structure-based antibody design has been limited to a relatively small number of cases where accurate structures or models of both the target antigen and antibody were available. As we move towards a time where it is possible to accurately model most antibodies and antigens, and to reliably predict their binding site, there is vast potential for true computational antibody design. In this review, we describe the latest methods that promise to launch a paradigm shift towards entirely in silico structure-based antibody design.

Design and Analysis of Pharmacokinetics, Pharmacodynamics and Toxicological analysis of Cannabidiol analogs using in silico Tools

Background:

Background: Cannabidiol (CBD), a non‐psychoactive phytocannabinoid from Cannabis Sativa, has become an interesting option for medicinal chemists in the development of new drug candidates

Objective:

Objective: This study aims to propose analogs with therapeutic potential from the CBD scaffold. Methods: The 16 analogs proposed were designed using the PubChem Sketcher V. 2.4® software. Already, CBD analogs were subjected to different in silico tools, such as Molinspiration®; SwissADME®; SwissTargetPrediction® and OSIRIS Property Explorer

Results and Discussio:

Results and Discussion: The screening of CBD analogs carried out in this study showed compounds 9 and 16 with good affinity for interactions with CB1 and CB2 receptors. Pharmacokinetic data showed that these two compounds have good oral absorption. Finally, in silico toxicity data showed that these compounds pose no risk of a toxic event in humans

Conclusion:

Conclusion: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.

Poly-Lysine Dendritic Nanocarrier to Target Epidermal Growth Factor Receptor Overexpressed Breast Cancer for Methotrexate Delivery

A fourth generation poly-lysine dendritic nanocarrier (P₄LDN)-based targeted chemotherapy for breast cancer is attempted by incorporating an epidermal growth factor receptor (EGFR)-specific short peptide E2 (ARSHVGYTGAR) and the drug methotrexate (MTX) into a nanocarrier system. The drug is incorporated into the nanocarrier using a cathepsin B cleavable spacer: glycine–phenylalanine–leucine–glycine (GFLG). The in vitro analysis of the time-dependent drug release, binding and internalization ability, and the cytotoxic nature showed that this drug delivery system (DDS) is highly effective. The efficacy analysis using non-obese diabetic/severe combined immunodeficiency (NOD-SCID) mice also showed that compared to the control group, the DDS can effectively reduce tumor volume. The mice that received the DDS appeared to gain weight more rapidly than the free drug, which suggests that the dendrimer is more easily tolerated by mice than the free drug.

Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction

Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and specificity. This malleability of binding makes antibodies an important class of biological drugs and biomarkers. In this article, we propose a method to identify which amino acid residues of an antibody directly interact with its associated antigen based on the features from sequence and structure. Our algorithm uses convolution neural networks (CNNs) linked with graph convolution networks (GCNs) to make use of information from both sequential and spatial neighbors to understand more about the local environment of target amino acid residue. Furthermore, we process the antigen partner of an antibody by employing an attention layer. Our method improves on the state-of-the-art methodology.

Treatment of Connective Tissue Disease-Related Intractable Disease with Biological Therapeutics

The treatment of connective tissue disease (CTD) and CTD-related intractable diseases (CTD-IDs) currently depends on the use of steroid therapy. Approximately 20 years have passed since the approval of infliximab for rheumatoid arthritis in 2003. Since then, several biological therapeutics have been marketed and adapted for many CTDs and CTD-IDs other than rheumatoid arthritis. Although conventional treatment for patients with these diseases is rarely used because of their poor prognosis, these cases may benefit from biological therapeutics. However, choosing biological therapeutics is difficult because they have different target molecules compared with conventional therapeutics. In this review, we address the current situation of biological therapeutics for CTD-IDs including Behcet's disease, psoriatic arthritis, ankylosing spondylitis, anti-neutrophil cytoplasmic antibody-related arthritis, and adult Still's disease, as well as the choice of biological therapeutics in clinical practice.

Exploring antibody repurposing for COVID-19: beyond presumed roles of therapeutic antibodies

The urgent need for a treatment of COVID-19 has left researchers with limited choice of either developing an effective vaccine or identifying approved/investigational drugs developed for other medical conditions for potential repurposing, thus bypassing long clinical trials. In this work, we compared the sequences of experimentally verified SARS-CoV-2 neutralizing antibodies and sequentially/structurally similar commercialized therapeutic monoclonal antibodies. We have identified three therapeutic antibodies, Tremelimumab, Ipilimumab and Afasevikumab. Interestingly, these antibodies target CTLA4 and IL17A, levels of which have been shown to be elevated during severe SARS-CoV-2 infection. The candidate antibodies were evaluated further for epitope restriction, interaction energy and interaction surface to gauge their repurposability to tackle SARS-CoV-2 infection. Our work provides candidate antibody scaffolds with dual activities of plausible viral neutralization and immunosuppression. Further, these candidate antibodies can also be explored in diagnostic test kits for SARS-CoV-2 infection. We opine that this in silico workflow to screen and analyze antibodies for repurposing would have widespread applications.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Designing and constructing a phage display synthesized single domain antibodies library based on camel VHHs frame for screening and identifying humanized TNF-α-specific nanobody

Tumor necrosis factor (TNF-α) is an important clinically tested cytokine that could induce autoimmune diseases and inflammation. Therefore, the anti-TNF-α therapy strategy was developed and used therapeutically in various diseases, especially in the cytokine storm associated chimeric antigen receptor (CAR) T-cell therapy and antiviral therapy. Compare with other anti-TNF-α inhibitors, anti-TNF-α Nb (nanobody) has many unique advantages. Herein, we reported a novel humanized scaffold for library construction, which could be soluble and expressed in Escherichia coli (E.coli), and the efficiency capacity could reach as high as 2.01 × 10⁹. Meanwhile, an anti-TNF-α Nb was selected for further study after 4 rounds of screening, NT-3, as the optimal Nb could effectively inhibit TNF-mediated cytotoxicity. The IC50 of NT-3 was determined as 0.804 μM, and its apoptosis inhibition rate was 62.47 % in L929 cells. Furthermore, the molecular docking results showed that complementarity-determining regions (CDRs) of NT-3 could connect to TNF for blocking function through strong hydrogen bonds and salt bridges. In general, our study not only provided a good Nb screening platform in vitro without animal immunization, but also generated a series of novel humanized anti-TNF-α Nb candidates with potential applications.

Residue-based pharmacophore approaches to study protein-protein interactions

This review focuses on pharmacophore approaches in researching protein interfaces that bind protein ligands. Pharmacophore descriptions of binding interfaces that employ molecular dynamics simulation can account for effects of solvation and conformational flexibility. In addition, these calculations provide an approximation to entropic considerations and as such, a better approximation of the free energy of binding. Residue-based pharmacophore approaches can facilitate a variety of drug discovery tasks such as the identification of receptor-ligand partners, identifying their binding poses, designing protein interfaces for selectivity, or defining a reduced mutational combinatorial exploration for subsequent experimental engineering techniques by orders of magnitudes.

Therapeutic peptides for the treatment of cystic fibrosis: Challenges and perspectives

Cystic fibrosis (CF) is the most common amongst rare genetic diseases, affecting more than 70.000 people worldwide. CF is characterized by a dysfunctional chloride channel, termed cystic fibrosis conductance regulator (CFTR), which leads to the production of a thick and viscous mucus layer that clogs the lungs of CF patients and traps pathogens, leading to chronic infections and inflammation and, ultimately, lung damage. In recent years, the use of peptides for the treatment of respiratory diseases, including CF, has gained growing interest. Therapeutic peptides for CF include antimicrobial peptides, inhibitors of proteases, and modulators of ion channels, among others. Peptides display unique features that make them appealing candidates for clinical translation, like specificity of action, high efficacy, and low toxicity. Nevertheless, the intrinsic properties of peptides, together with the need of delivering these compounds locally, e.g. by inhalation, raise a number of concerns in the development of peptide therapeutics for CF lung disease. In this review, we discuss the challenges related to the use of peptides for the treatment of CF lung disease through inhalation, which include retention within mucus, proteolysis, immunogenicity and aggregation. Strategies for overcoming major shortcomings of peptide therapeutics will be presented, together with recent developments in peptide design and optimization, including computational analysis and high-throughput screening.

Druggable targets from coronaviruses for designing new antiviral drugs

Severe respiratory infections were highlighted in the SARS-CoV outbreak in 2002, as well as MERS-CoV, in 2012. Recently, the novel CoV (COVID-19) has led to severe respiratory damage to humans and deaths in Asia, Europe, and Americas, which allowed the WHO to declare the pandemic state. Notwithstanding all impacts caused by Coronaviruses, it is evident that the development of new antiviral agents is an unmet need. In this review, we provide a complete compilation of all potential antiviral agents targeting macromolecular structures from these Coronaviruses (Coronaviridae), providing a medicinal chemistry viewpoint that could be useful for designing new therapeutic agents.

mCSM-AB2: guiding rational antibody design using graph-based signatures

MOTIVATION

A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity.

RESULTS

Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering.

AVAILABILITY AND IMPLEMENTATION

mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antibody interface prediction with 3D Zernike descriptors and SVM

MOTIVATION

Antibodies are a class of proteins capable of specifically recognizing and binding to a virtually infinite number of antigens. This binding malleability makes them the most valuable category of biopharmaceuticals for both diagnostic and therapeutic applications. The correct identification of the antigen-binding residues in the antibody is crucial for all antibody design and engineering techniques and could also help to understand the complex antigen binding mechanisms. However, the antibody-binding interface prediction field appears to be still rather underdeveloped.

RESULTS

We present a novel method for antibody interface prediction from their experimentally solved structures based on 3D Zernike Descriptors. Roto-translationally invariant descriptors are computed from circular patches of the antibody surface enriched with a chosen subset of physico-chemical properties from the AAindex1 amino acid index set, and are used as samples for a binary classification problem. An SVM classifier is used to distinguish interface surface patches from non-interface ones. The proposed method was shown to outperform other antigen-binding interface prediction software.

AVAILABILITY AND IMPLEMENTATION

Linux binaries and Python scripts are available at https://github.com/sebastiandaberdaku/AntibodyInterfacePrediction. The datasets generated and/or analyzed during the current study are available at https://doi.org/10.6084/m9.figshare.5442229.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Understanding microcystin-LR antibody binding interactions using in silico docking and in vitro mutagenesis

Microcystins (MCs) are a group of highly potent cyanotoxins that are becoming more widely distributed due to increased global temperatures and climate change. Microcystin-leucine-arginine (MC-LR) is the most potent and most common variant, with a guideline limit of 1 μg/l in drinking water. We previously developed a novel avian single-chain fragment variable (scFv), designated 2G1, for use in an optical-planar waveguide detection system for microcystin determination. This current work investigates interactions between 2G1 and MC-LR at the molecular level through modelling with an avian antibody template and molecular docking by AutoDock Vina to identify key amino acid (AA) residues involved. These potential AA interactions were investigated in vitro by targeted mutagenesis, specifically, by alanine scanning mutations. Glutamic acid (E) was found to play a critical role in the 2G1-MC-LR binding interaction, with the heavy chain glutamic acid (E) 102 (H-E102) forming direct bonds with the arginine (R) residue of MC-LR. In addition, alanine mutation of light chain residue aspartic acid 57 (L-D57) led to an improvement in antigen-binding observed using enzyme-linked immunosorbent assay (ELISA), and was confirmed by surface plasmon resonance (SPR). This work will contribute to improving the binding of recombinant anti-MC-LR to its antigen and aid in the development of a higher sensitivity harmful algal toxin diagnostic.

Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations

Custom-designed ligand-binding proteins represent a promising class of macromolecules with exciting applications toward the design of new enzymes or the engineering of antibodies and small-molecule recruited proteins for therapeutic interventions. However, several challenges remain in designing a protein sequence such that the binding site organization results in high affinity interaction with a bound ligand. Here, we study the dynamics of explicitly solvated designed proteins through all-atom molecular dynamics (MD) simulations to gain insight into the causes that lead to the low affinity or instability of most of these designs, despite the prediction of their success by the computational design methodology. Simulations ranging from 500 to 1000 ns per replicate were conducted on 37 designed protein variants encompassing two distinct folds and a range of ligand affinities, resulting in more than 180 μs of combined sampling. The simulations provide retrospective insights into the properties affecting ligand affinity that can prove useful in guiding further steps of design optimization. Features indicate that entropic components are particularly important for affinity, which are not easily incorporated in the empirical models often used in design protocols. Additionally, we demonstrate that the application of machine learning approaches built upon the output from the simulations can help discriminate between successful and failed binders, such that MD could act as a screening step in protein design, resulting in a more efficient process.

From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling

Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.

AbRSA: A robust tool for antibody numbering

The remarkable progress in cancer immunotherapy in recent years has led to the heat of great development for therapeutic antibodies. Antibody numbering, which standardizes a residue index at each position of an antibody variable domain, is an important step in immunoinformatic analysis. It provides an equivalent index for the comparison of sequences or structures, which is particularly valuable for antibody modeling and engineering. However, due to the extremely high diversity of antibody sequences, antibody-numbering tools cannot work in all cases. This article introduces a new antibody-numbering tool named AbRSA, which integrates heuristic knowledge of region-specific features into sequence mapping to enhance the robustness. The benchmarks demonstrate that, AbRSA exhibits robust performance in numbering sequences with diverse lengths and patterns compared with the state-of-the-art tools. AbRSA offers a user-friendly interface for antibody numbering, complementarity-determining region delimitation, and 3D structure rendering. It is freely available at http://cao.labshare.cn/AbRSA.

Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design

Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed.

Cognizance of Molecular Methods for the Generation of Mutagenic Phage Display Antibody Libraries for Affinity Maturation

Antibodies leverage on their unique architecture to bind with an array of antigens. The strength of interaction has a direct relation to the affinity of the antibodies towards the antigen. In vivo affinity maturation is performed through multiple rounds of somatic hypermutation and selection in the germinal centre. This unique process involves intricate sequence rearrangements at the gene level via molecular mechanisms. The emergence of in vitro display technologies, mainly phage display and recombinant DNA technology, has helped revolutionize the way antibody improvements are being carried out in the laboratory. The adaptation of molecular approaches in vitro to replicate the in vivo processes has allowed for improvements in the way recombinant antibodies are designed and tuned. Combinatorial libraries, consisting of a myriad of possible antibodies, are capable of replicating the diversity of the natural human antibody repertoire. The isolation of target-specific antibodies with specific affinity characteristics can also be accomplished through modification of stringent protocols. Despite the ability to screen and select for high-affinity binders, some 'fine tuning' may be required to enhance antibody binding in terms of its affinity. This review will provide a brief account of phage display technology used for antibody generation followed by a summary of different combinatorial library characteristics. The review will focus on available strategies, which include molecular approaches, next generation sequencing, and in silico approaches used for antibody affinity maturation in both therapeutic and diagnostic applications.

In Silico Analysis of Homologous Heterodimers of Cruzipain-Chagasin from Structural Models Built by Homology

The present study gives an overview of the binding energetics of the homologous heterodimers of cruzipain−chagasin based on the binding energy (ΔG_b) prediction obtained with FoldX. This analysis involves a total of 70 homologous models of the cruzipain−chagasin complex which were constructed by homology from the combinatory variation of nine papain-like cysteine peptidase structures and seven cysteine protease inhibitor structures (as chagasin-like and cystatin-like inhibitors). Only 32 systems have been evaluated experimentally, ΔG_b^experimental values previously reported. Therefore, the result of the multiple analysis in terms of the thermodynamic parameters, are shown as relative energy |ΔΔG| = |ΔG_b^fromFoldX − ΔG_b^experimental|. Nine models were identified that recorded |ΔΔG| < 1.3, five models to 2.8 > |ΔΔG| > 1.3 and the other 18 models, values of |ΔΔG| > 2.8. The energetic analysis of the contribution of ΔH and ΔS to ΔG_b to the 14-molecular model presents a ΔG_b mostly ΔH-driven at neutral pH and at an ionic strength (I) of 0.15 M. The dependence of ΔG_b(I,pH) at 298 K to the cruzipain−chagasin complex predicts a linear dependence of ΔG_b(I). The computational protocol allowed the identification and prediction of thermodynamics binding energy parameters for cruzipain−chagasin-like heterodimers.

The Biologics Revolution and Endotoxin Test Concerns

The advent of “at will” production of biologics in lieu of harvesting animal proteins (i.e. insulin) or human cadaver proteins (i.e. growth hormone) has revolutionized the treatment of disease. While the fruits of the biotechnology revolution are widely acknowledged, the realization of the differences in the means of production and changes in the manner of control of potential impurities and contaminants in regard to the new versus the old are less widely appreciated. This chapter is an overview of the biologics revolution in terms of the rigors of manufacturing required to produce them, their mechanism of action, and caveats of endotoxin control. It is a continulation of the previous chapter that established a basic background knowledge of adaptive immune principles necessary to understand the mode of action of both disease causation and biologics therapeutic treatment via immune modulation.

Biotherapeutics: Challenges and Opportunities for Predictive Toxicology of Monoclonal Antibodies

Biotherapeutics are a rapidly growing portion of the total pharmaceutical market accounting for almost one-half of recent new drug approvals. A major portion of these approvals each year are monoclonal antibodies (mAbs). During development, non-clinical pharmacology and toxicology testing of mAbs differs from that done with chemical entities since these biotherapeutics are derived from a biological source and therefore the animal models must share the same epitopes (targets) as humans to elicit a pharmacological response. Mechanisms of toxicity of mAbs are both pharmacological and non-pharmacological in nature; however, standard in silico predictive toxicological methods used in research and development of chemical entities currently do not apply to these biotherapeutics. Challenges and potential opportunities exist for new methodologies to provide a more predictive program to assess and monitor potential adverse drug reactions of mAbs for specific patients before and during clinical trials and after market approval.

Novel Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Oncorhynchus mykiss Nebulin: Virtual Screening and In Silico Molecular Docking Study

Excessive concentrations of angiotensin-converting enzyme (ACE) can give rise to high blood pressure, and is harmful to the body. ACE inhibitory peptides from food proteins are considered good sources of function food. However, the preparation of ACE inhibitory peptides by classical method faces many challenges. Three novel ACE inhibitory peptides were identified by in silico methods, and showed potent activity against ACE in vitro. The simulation hydrolysis of nebulin was performed with ExPASy PeptideCutter program. Potential activity, solubility, and absorption, distribution, metabolism, excretion, and toxicity properties of generated peptides were predicted using program online. Molecular docking displayed that EGF, HGR, and VDF were docked into the S₁ and S₂ pockets of ACE. Meanwhile, Phe and Arg at the C-terminal enhance ACE affinity. The IC₅₀ values of EGF, HGR, and VDF were 474.65 ± 0.08, 106.21 ± 0.52, and 439.27 ± 0.09 μM, respectively. Three peptides EGF, HGR, and VDF from Oncorhynchus mykiss nebulin were identified, and the molecular mechanism between ACE and peptides was clarified using in silico methods. The results suggested that Oncorhynchus mykiss nebulin would be an attractive raw material of antihypertensive nutraceutical ingredients.

PRACTICAL APPLICATION

This study has shown the potential of Oncorhynchus mykiss nebulin as good sources for producing ACE inhibitory peptides. According to this finding, in silico approach is the feasible way for prediction and identification of food-derived ACE inhibitory peptides in emerging nutraceutical field.

Computer-aided design of amino acid-based therapeutics: a review

During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.

Identifying the Epitope Regions of Therapeutic Antibodies Based on Structure Descriptors

Therapeutic antibodies are widely used for disease detection and specific treatments. However, as an exogenous protein, these antibodies can be detected by the human immune system and elicit a response that can lead to serious illnesses. Therapeutic antibodies can be engineered through antibody humanization, which aims to maintain the specificity and biological function of the original antibodies, and reduce immunogenicity. However, the antibody drug effect is synchronously reduced as more exogenous parts are replaced by human antibodies. Hence, a major challenge in this area is to precisely detect the epitope regions in immunogenic antibodies and guide point mutations of exogenous antibodies to balance both humanization level and drug effect. In this article, the latest dataset of immunoglobulin complexes was collected from protein data bank (PDB) to discover the spatial features of immunogenic antibody. Furthermore, a series of structure descriptors were generated to characterize and distinguish epitope residues from non-immunogenic regions. Finally, a computational model was established based on structure descriptors, and results indicated that this model has the potential to precisely predict the epitope regions of therapeutic antibodies. With rapid accumulation of immunoglobulin complexes, this methodology could be used to improve and guide future antibody humanization and potential clinical applications.