Accelerating therapeutic protein design with computational approaches toward the clinical stage

Advancing Alzheimer's Therapy: Computational strategies and treatment innovations

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlighting the need for innovative therapeutic approaches. This review explores the challenges of AD treatment and the integration of computational methodologies to advance therapeutic interventions. A comprehensive analysis of recent literature was conducted to elucidate the broad scope of Alzheimer's etiology and the limitations of conventional drug discovery approaches. Our findings underscore the critical role of computational models in elucidating disease mechanisms, identifying therapeutic targets, and expediting drug discovery. Through computational simulations, researchers can predict drug efficacy, optimize lead compounds, and facilitate personalized medicine approaches. Moreover, machine learning algorithms enhance early diagnosis and enable precision medicine strategies by analyzing multi-modal datasets. Case studies highlight the application of computational techniques in AD therapeutics, including the suppression of crucial proteins implicated in disease progression and the repurposing of existing drugs for AD management. Computational models elucidate the interplay between oxidative stress and neurodegeneration, offering insights into potential therapeutic interventions. Collaborative efforts between computational biologists, pharmacologists, and clinicians are essential to translate computational insights into clinically actionable interventions, ultimately improving patient outcomes and addressing the unmet medical needs of individuals affected by AD. Overall, integrating computational methodologies represents a promising paradigm shift in AD therapeutics, offering innovative solutions to overcome existing challenges and transform the landscape of AD treatment.

Engineering Affibody Binders to Death Receptor 5 and Tumor Necrosis Factor Receptor 1 With Improved Stability

Protein developability is an important, yet often overlooked, aspect of protein discovery campaigns that is a key driver of utility. Recent advances have improved developability screening capacity, making it an increasingly viable option in early-stage discovery. Here, we engineered one component of developability, stability, of two affibody proteins-one that targets death receptor 5 and another that targets tumor necrosis factor receptor 1-previously evolved to bind receptor and non-competitively inhibit signaling via conformational modulation. Starting from an error-prone PCR library of each affibody, variants were screened via yeast surface display binder selections, including depletion of non-specific binders, followed by developability assessment using the on-yeast protease and yeast display level assays. Multiplex deep sequencing identified variants for further evaluation. Purified variants exhibited elevated stability-8°C to 14°C increase in Tm,app-with maintained 1-2 nM affinity for the TNFR1 affibody and 30-fold improvement in the DR5 affibody affinity to 0.8 nM.

Additivity of Transfer Free Energies Enables the Description of Complex Protein Formulations in Implicit Solvent Molecular Dynamics Simulations

A complex 3D structure and the surrounding environment determine the function and stability of a protein. Various osmolytes can be added to a protein drug formulation to stabilize the native protein structure by preventing unfolding and aggregation. In this context, the concept of transfer free energy, which represents the change in chemical potential of a protein being transferred from water to an osmolyte solution, has emerged as a powerful tool to elucidate the energetics involved in the protein-osmolyte interaction. In the present work, we experimentally determine the transfer free energies for the excipients sodium chloride, arginine hydrochloride, and polysorbate 20, which are frequently used in pharmaceutical protein formulations. We show that these excipients display distinct patterns of exclusion or interaction toward different moieties on the protein surface. Furthermore, we report that the free energy cost for transferring a protein to a formulation composed of multiple components can be calculated by summing up the contributions of the individual components. This finding suggests that additivity applies to the transfer free energies. We demonstrate that this additive behavior can be leveraged to accurately and efficiently model complex protein formulations. Additionally, we discuss how transfer free energies can be incorporated within implicit solvent molecular dynamics calculations, providing a direct link between experiments and simulations. Our molecular dynamics results show good agreement with experimental data for lysozyme, interferon α-2a, and granulocyte colony-stimulating factor, for both single- and multicomponent matrices, demonstrating the validity of our approach.

The assembled decoders to prepare for "bioactive X″ against progressive deterioration of liver disease: From NAFLD to HCC

Non-alcoholic fatty liver disease (NAFLD) is implicated in steatohepatitis (NASH), liver cirrhosis (LC) to hepatocellular carcinoma (HCC), sequentially. Herein, our aim was to unravel the nuanced key components (compounds, and targets) to deter the progressive severity concerning hepatocellular diseases. We incorporated rigor bioinformatics and computational screening tools to decode effector(s) against NAFLD, NASH, LC, and HCC. The corresponding ligands of PDX1 (transcription factor of INS; one agonist), and IL6 (thirty-two antagonists) were identified by Selleckchem. Molecular docking test (MDT) revealed that PDX1- BRD7552 conformer (-12.1 kcal/mol), and IL6- Forsythoside B (-11.4 kcal/mol) conformer formed most stable complex. In parallel, DFT proposed that BRD7552, and Forsythoside B had significant chemical properties to react the targets, respectively. In conclusion, we decoded causatives of the progressive liver disease with web-based tools in drug repositioning theory. BRD7552 as PDX1 agonist, and Forsythoside B as IL6 antagonist were attributed to synergistic efficacy against NAFLD-derived HCC.

Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools

Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses against foreign antigens and, in some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements have enhanced therapeutic interventions, the integration of artificial intelligence (AI) is revolutionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, AI enables the prediction and design of antibody sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, and antigen-antibody interactions. These AI-powered innovations address longstanding challenges in antibody development, significantly improving speed, specificity, and accuracy in therapeutic design. By integrating computational advancements with biomedical applications, AI is driving next-generation cancer therapies, transforming precision medicine, and enhancing patient outcomes.

Modulating the complement system through epitope-specific inhibition by complement C3 inhibitors

As an integral part of the innate immune system, the complement system is a tightly regulated proteolytic cascade, playing a critical role in microbial defense, inflammation activation, and dying host cell clearance. Complement proteins are now emerging as subjects of intense research and drug development, since dysregulation of the complement system plays a critical role in several diseases and disorders, such as paroxysmal nocturnal hemoglobinuria (PNH) and geographic atrophy (GA). Within the complement cascade, complement C3 is the central component, situated at the convergence of all complement activation pathways, rendering it an attractive target for complement-related diseases. However, due to the complicated structure-activity relationship (SAR) of C3, elucidating the mechanisms of C3 inhibition on diverse epitopes is the basis for the rational design of C3-targeted therapeutics. Here, we have developed a set of comprehensive biochemical assays that are tailored to the specific steps within the complement cascade, allowing for a thorough understanding of the pharmacological consequences of different C3 inhibitors at each stage. Utilizing three model inhibitors (MIs) with different epitopes, we found that inhibition of MG4/MG5 domains has potent inhibition efficacy across all the complement activation pathways by interrupting C3-C3 convertase interaction, while inhibition of C345C domain displays a bias over the Alternative pathway (AP) inhibition by impairing AP C3 proconvertase formation. This study elucidates the intricate impact of C3 inhibition by targeting different epitopes, offering valuable insights into understanding the mechanism and facilitating the rational design of C3-targeted therapeutics.

Platelet-Type von Willebrand Disease: Complex Pathophysiology and Insights on Novel Therapeutic and Diagnostic Strategies

von Willebrand disease (VWD) is the most common well-studied genetic bleeding disorder worldwide. Much less is known about platelet-type VWD (PT-VWD), a rare platelet function defect, and a "nonidentical" twin bleeding phenotype to type 2B VWD (2B-VWD). Rather than a defect in the von Willebrand factor (VWF) gene, PT-VWD is caused by a platelet GP1BA mutation leading to a hyperaffinity of the glycoprotein Ibα (GPIbα) platelet surface receptor for VWF, and thus increased platelet clearing and high-molecular-weight VWF multimer elimination. Nine GP1BA gene mutations are known. It is historically believed that this enhanced binding was enabled by the β-switch region of GPIbα adopting an extended β-hairpin form. Recent evidence suggests the pathological conformation that destabilizes the compact triangular form of the R-loop-the GPIbα protein's region for VWF binding. PT-VWD is often misdiagnosed as 2B-VWD, even the though distinction between the two is crucial for proper treatment, as the former requires platelet transfusions, while the latter requires VWF/FVIII concentrate administration. Nevertheless, these PT-VWD treatments remain unsatisfactory, owing to their high cost, low availability, risk of alloimmunity, and the need to carefully balance platelet administration. Antibodies such as 6B4 remain undependable as an alternative therapy due to their questionable efficacy and high costs for this purpose. On the other hand, synthetic peptide therapeutics developed with In-Silico Protein Synthesizer to disrupt the association between GPIbα and VWF show preliminary promise as a therapy based on in vitro experiments. Such peptides could serve as an effective diagnostic technology for discriminating between 2B-VWD and PT-VWD, or potentially all forms of VWD, based on their high specificity. This field is rapidly growing and the current review sheds light on the complex pathology and some novel potential therapeutic and diagnostic strategies.

Beyond In Vivo, Pharmaceutical Molecule Production in Cell-Free Systems and the Use of Noncanonical Amino Acids Therein

Throughout history, we have looked to nature to discover and copy pharmaceutical solutions to prevent and heal diseases. Due to the advances in metabolic engineering and the production of pharmaceutical proteins in different host cells, we have moved from mimicking nature to the delicate engineering of cells and proteins. We can now produce novel drug molecules, which are fusions of small chemical drugs and proteins. Currently we are at the brink of yet another step to venture beyond nature's border with the use of unnatural amino acids and manufacturing without the use of living cells using cell-free systems. In this review, we summarize the progress and limitations of the last decades in the development of pharmaceutical protein development, production in cells, and cell-free systems. We also discuss possible future directions of the field.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

3D physiologically-informed deep learning for drug discovery of a novel vascular endothelial growth factor receptor-2 (VEGFR2)

Angiogenesis is an essential process in tumorigenesis, tumor invasion, and metastasis, and is an intriguing pathway for drug discovery. Targeting vascular endothelial growth factor receptor 2 (VEGFR2) to inhibit tumor angiogenic pathways has been widely explored and adopted in clinical practice. However, most drugs, such as the Food and Drug Administration -approved drug axitinib (ATC code: L01EK01), have considerable side effects and limited tolerability. Therefore, there is an urgent need for the development of novel VEGFR2 inhibitors. In this study, we propose a novel strategy to design potential candidates targeting VEGFR2 using three-dimensional (3D) deep learning and structural modeling methods. A geometric-enhanced molecular representation learning method (GEM) model employing a graph neural network (GNN) as its underlying predictive algorithm was used to predict the activity of the candidates. In the structural modeling method, flexible docking was performed to screen data with high affinity and explore the mechanism of the inhibitors. Small -molecule compounds with consistently improved properties were identified based on the intersection of the scores obtained from both methods. Candidates identified using the GEM-GNN model were selected for in silico modeling using molecular dynamics simulations to further validate their efficacy. The GEM-GNN model enabled the identification of candidate compounds with potentially more favorable properties than the existing drug, axitinib, while achieving higher efficacy.

Integrating Computational Design and Experimental Approaches for Next-Generation Biologics

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

Suborgan Level Quantitation of Proteins in Tissues Delivered by Polymeric Nanocarriers

Amidst the rapid growth of protein therapeutics as a drug class, there is an increased focus on designing systems to effectively deliver proteins to target organs. Quantitative monitoring of protein distributions in tissues is essential for optimal development of delivery systems; however, existing strategies can have limited accuracy, making it difficult to assess suborgan dosing. Here, we describe a quantitative imaging approach that utilizes metal-coded mass tags and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to quantify the suborgan distributions of proteins in tissues that have been delivered by polymeric nanocarriers. Using this approach, we measure nanomole per gram levels of proteins as delivered by guanidinium-functionalized poly(oxanorborneneimide) (PONI) polymers to various tissues, including the alveolar region of the lung. Due to the multiplexing capability of the LA-ICP-MS imaging, we are also able to simultaneously quantify protein and polymer distributions, obtaining valuable information about the relative excretion pathways of the protein cargo and carrier. This imaging approach will facilitate quantitative correlations between nanocarrier properties and protein cargo biodistributions.

Therapeutic proteins: developments, progress, challenges, and future perspectives

Proteins are considered magic molecules due to their enormous applications in the health sector. Over the past few decades, therapeutic proteins have emerged as a promising treatment option for various diseases, particularly cancer, cardiovascular disease, diabetes, and others. The formulation of protein-based therapies is a major area of research, however, a few factors still hinder the large-scale production of these therapeutic products, such as stability, heterogenicity, immunogenicity, high cost of production, etc. This review provides comprehensive information on various sources and production of therapeutic proteins. The review also summarizes the challenges currently faced by scientists while developing protein-based therapeutics, along with possible solutions. It can be concluded that these proteins can be used in combination with small molecular drugs to give synergistic benefits in the future.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.