Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Bioactive peptides and proteins for tissue repair: microenvironment modulation, rational delivery, and clinical potential

Bioactive peptides and proteins (BAPPs) are promising therapeutic agents for tissue repair with considerable advantages, including multifunctionality, specificity, biocompatibility, and biodegradability. However, the high complexity of tissue microenvironments and their inherent deficiencies such as short half-live and susceptibility to enzymatic degradation, adversely affect their therapeutic efficacy and clinical applications. Investigating the fundamental mechanisms by which BAPPs modulate the microenvironment and developing rational delivery strategies are essential for optimizing their administration in distinct tissue repairs and facilitating clinical translation. This review initially focuses on the mechanisms through which BAPPs influence the microenvironment for tissue repair via reactive oxygen species, blood and lymphatic vessels, immune cells, and repair cells. Then, a variety of delivery platforms, including scaffolds and hydrogels, electrospun fibers, surface coatings, assisted particles, nanotubes, two-dimensional nanomaterials, and nanoparticles engineered cells, are summarized to incorporate BAPPs for effective tissue repair, modification strategies aimed at enhancing loading efficiencies and release kinetics are also reviewed. Additionally, the delivery of BAPPs can be precisely regulated by endogenous stimuli (glucose, reactive oxygen species, enzymes, pH) or exogenous stimuli (ultrasound, heat, light, magnetic field, and electric field) to achieve on-demand release tailored for specific tissue repair needs. Furthermore, this review focuses on the clinical potential of BAPPs in facilitating tissue repair across various types, including bone, cartilage, intervertebral discs, muscle, tendons, periodontal tissues, skin, myocardium, nervous system (encompassing brain, spinal cord, and peripheral nerve), endometrium, as well as ear and ocular tissue. Finally, current challenges and prospects are discussed.

Transformation of peptides to small molecules in medicinal chemistry: Challenges and opportunities

Peptides are native binders involved in numerous physiological life procedures, such as cellular signaling, and serve as ready-made regulators of biochemical processes. Meanwhile, small molecules compose many drugs owing to their outstanding advantages of physiochemical properties and synthetic convenience. A novel field of research is converting peptides into small molecules, providing a convenient portable solution for drug design or peptidomic research. Endowing properties of peptides onto small molecules can evolutionarily combine the advantages of both moieties and improve the biological druggability of molecules. Herein, we present eight representative recent cases in this conversion and elaborate on the transformation process of each case. We discuss the innovative technological methods and research approaches involved, and analyze the applicability conditions of the approaches and methods in each case, guiding further modifications of peptides to small molecules. Finally, based on the aforementioned cases, we summarize a general procedure for peptide-to-small molecule modifications, listing the technological methods available for each transformation step and providing our insights on the applicable scenarios for these methods. This review aims to present the progress of peptide-to-small molecule modifications and propose our thoughts and perspectives for future research in this field.

A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Modeling Peptide-Protein Interactions by a Logo-Based Method: Application in Peptide-HLA Binding Predictions

Peptide-protein interactions form a cornerstone in molecular biology, governing cellular signaling, structure, and enzymatic activities in living organisms. Improving computational models and experimental techniques to describe and predict these interactions remains an ongoing area of research. Here, we present a computational method for peptide-protein interactions' description and prediction based on leveraged amino acid frequencies within specific binding cores. Utilizing normalized frequencies, we construct quantitative matrices (QMs), termed 'logo models' derived from sequence logos. The method was developed to predict peptide binding to HLA-DQ2.5 and HLA-DQ8.1 proteins associated with susceptibility to celiac disease. The models were validated by more than 17,000 peptides demonstrating their efficacy in discriminating between binding and non-binding peptides. The logo method could be applied to diverse peptide-protein interactions, offering a versatile tool for predictive analysis in molecular binding studies.

Assessment of the bound conformation of bombesin to the BB1 and BB2 receptors

Bombesin is an endogenous peptide involved in a wide spectrum of physiological activities ranging from satiety, control of circadian rhythm and thermoregulation in the central nervous system, to stimulation of gastrointestinal hormone release, activation of macrophages and effects on development in peripheral tissues. Actions of the peptide are mediated through the two high affinity G-protein coupled receptors BB1R and BB2R. Under pathophysiological conditions, these receptors are overexpressed in many different types of tumors, such as prostate cancer, breast cancer, small and non-small cell lung cancer and pancreatic cancer. This observation has been used for designing cell markers, but it has not been yet exploited for therapeutical purposes. Despite the enormous biological interest of the peptide, little is known about the stereochemical features that contribute to their activity. On the one hand, mutagenesis studies identified a few receptor residues important for high bombesin affinity and on the other, a few studies focused on the relevance of diverse residues of the peptide for receptor activation. Models of the peptide bound to BB1R and BB2R can be helpful to improve our understanding of the stereochemical features granting bombesin activity. Accordingly, the present study describes the computational process followed to construct such models by means of Steered Molecular Dynamics, using models of the peptide and its receptors. Present results provide new insights into the structure-activity relationships of bombesin and its receptors, as well as render an explanation for the differential binding affinity observed towards BB1R and BB2R. Finally, these models can be further exploited to help for designing novel small molecule peptidomimetics with improved pharmacokinetics profile.

Selective inhibitors targeting Fis1/Mid51 protein-protein interactions protect against hypoxia-induced damage in cardiomyocytes

Cardiovascular diseases (CVDs) are the most common non-communicable diseases globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Mitochondria play critical roles in cellular metabolic homeostasis, cell survival, and cell death, as well as producing most of the cell's energy. Protein-protein interactions (PPIs) have a significant role in physiological and pathological processes, and aberrant PPIs are associated with various diseases, therefore they are potential drug targets for a broad range of therapeutic areas. Due to their ability to mimic natural interaction motifs and cover relatively larger interaction region, peptides are very promising as PPI inhibitors. To expedite drug discovery, computational approaches are widely used for screening potential lead compounds. Here, we developed peptides that inhibit mitochondrial fission 1 (Fis1)/mitochondrial dynamics 51 kDa (Mid51) PPI to reduce the cellular damage that can lead to various human pathologies, such as CVDs. Based on a rational design approach we developed peptide inhibitors of the Fis1/Mid51 PPI. In silico and in vitro studies were done to evaluate the biological activity and molecular interactions of the peptides. Two peptides, CVP-241 and CVP-242 were identified based on low binding energy and molecular dynamics simulations. These peptides inhibit Fis1/Mid51 PPI (-1324.9 kcal mol-1) in docking calculations (CVP-241, -741.3 kcal mol-1, and CVP-242, -747.4 kcal mol-1), as well as in vitro experimental studies Fis1/Mid51 PPI (KD 0.054 µM) Fis1/Mid51 PPI + CVP-241 (KD 3.43 µM), and Fis1/Mid51 PPI + CVP-242 (KD 44.58 µM). Finally, these peptides have no toxicity to H9c2 cells, and they increase cell viability in cardiomyocytes (H9c2 cells). Consequently, the identified inhibitor peptides could serve as potent molecules in basic research and as leads for therapeutic development.

Drug discovery by targeting the protein-protein interactions involved in autophagy

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

Computational Study of Driving Forces in ATSP, PDIQ, and P53 Peptide Binding: C═O···C═O Tetrel Bonding Interactions at Work

Understanding the molecular interactions that drive peptide folding is crucial to chemistry and biology. In this study, we analyzed the role of CO···CO tetrel bonding (TtB) interactions in the folding mechanism of three different peptides (ATSP, pDIQ, and p53), which exhibit a different propensity to fold in an α helix motif. To achieve this goal, we used both a recently developed Bayesian inference approach (MELDxMD) and Quantum Mechanics (QM) calculations at the RI-MP2/def2-TZVP level of theory. These techniques allowed us to study the folding process and to evaluate the strength of the CO···CO TtBs as well as the synergies between TtBs and hydrogen-bonding (HB) interactions. We believe that the results derived from our study will be helpful for those scientists working in computational biology, peptide chemistry, and structural biology.

Development of Cyclic Peptides Targeting the Epidermal Growth Factor Receptor in Mesenchymal Triple-Negative Breast Cancer Subtype

Triple-negative breast cancer (TNBC) is an aggressive malignancy characterized by the lack of expression of estrogen and progesterone receptors and amplification of human epidermal growth factor receptor 2 (HER2). Being the Epidermal Growth Factor Receptor (EGFR) highly expressed in mesenchymal TNBC and correlated with aggressive growth behavior, it represents an ideal target for anticancer drugs. Here, we have applied the phage display for selecting two highly specific peptide ligands for targeting the EGFR overexpressed in MDA-MB-231 cells, a human TNBC cell line. Molecular docking predicted the peptide-binding affinities and sites in the extracellular domain of EGFR. The binding of the FITC-conjugated peptides to human and murine TNBC cells was validated by flow cytometry. Confocal microscopy confirmed the peptide binding specificity to EGFR-positive MDA-MB-231 tumor xenograft tissues and their co-localization with the membrane EGFR. Further, the peptide stimulation did not affect the cell cycle of TNBC cells, which is of interest for their utility for tumor targeting. Our data indicate that these novel peptides are highly specific ligands for the EGFR overexpressed in TNBC cells, and thus they could be used in conjugation with nanoparticles for tumor-targeted delivery of anticancer drugs.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Biomechanical Behaviors and Degradation Properties of Multilayered Polymer Scaffolds: The Phase Space Method for Bile Duct Design and Bioengineering

This article reports the electrospinning technique for the manufacturing of multilayered scaffolds for bile duct tissue engineering based on an inner layer of polycaprolactone (PCL) and an outer layer either of a copolymer of D,L-lactide and glycolide (PLGA) or a copolymer of L-lactide and ε-caprolactone (PLCL). A study of the degradation properties of separate polymers showed that flat PCL samples exhibited the highest resistance to hydrolysis in comparison with PLGA and PLCL. Irrespective of the liquid-phase nature, no significant mass loss of PCL samples was found in 140 days of incubation. The PLCL- and PLGA-based flat samples were more prone to hydrolysis within the same period of time, which was confirmed by the increased loss of mass and a significant reduction of weight-average molecular mass. The study of the mechanical properties of developed multi-layered tubular scaffolds revealed that their strength in the longitudinal and transverse directions was comparable with the values measured for a decellularized bile duct. The strength of three-layered scaffolds declined significantly because of the active degradation of the outer layer made of PLGA. The strength of scaffolds with the PLCL outer layer deteriorated much less with time, both in the axial (p-value = 0.0016) and radial (p-value = 0.0022) directions. A novel method for assessment of the physiological relevance of synthetic scaffolds was developed and named the phase space approach for assessment of physiological relevance. Two-dimensional phase space (elongation modulus and tensile strength) was used for the assessment and visualization of the physiological relevance of scaffolds for bile duct bioengineering. In conclusion, the design of scaffolds for the creation of physiologically relevant tissue-engineered bile ducts should be based not only on biodegradation properties but also on the biomechanical time-related behavior of various compositions of polymers and copolymers.

Assessing a computational pipeline to identify binding motifs to the α2β1 integrin

Integrins in the cell surface interact with functional motifs found in the extracellular matrix (ECM) that queue the cell for biological actions such as migration, adhesion, or growth. Multiple fibrous proteins such as collagen or fibronectin compose the ECM. The field of biomechanical engineering often deals with the design of biomaterials compatible with the ECM that will trigger cellular response (e.g., in tissue regeneration). However, there are a relative few number of known integrin binding motifs compared to all the possible peptide epitope sequences available. Computational tools could help identify novel motifs, but have been limited by the challenges in modeling the binding to integrin domains. We revisit a series of traditional and novel computational tools to assess their performance in identifying novel binding motifs for the I-domain of the α2β1 integrin.

Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments

As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC₅₀s are 7.86 ± 1.25 and 24.72 ± 1.94 μM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Towards rational computational peptide design

Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.

Engineered cells along with smart scaffolds: critical factors for improving tissue engineering approaches

In this review, gene delivery and its applications are discussed in tissue engineering (TE); also, new techniques such as the CRISPR-Cas9 system, synthetics biology and molecular dynamics simulation to improve the efficiency of the scaffolds have been studied. CRISPR-Cas9 is expected to make significant advances in TE in the future. The fundamentals of synthetic biology have developed powerful and flexible methods for programming cells via artificial genetic circuits. The combination of regenerative medicine and artificial biology allows the engineering of cells and organisms for use in TE, biomaterials, bioprocessing and scaffold development. The dynamics of protein adsorption at the scaffold surface at the atomic level can provide valuable guidelines for the future design of TE scaffolds /implants.

Glycosylation is key for enhancing drug recognition into spike glycoprotein of SARS-CoV-2

The emergence of COVID-19 caused by SARS-CoV-2 and its spread since 2019 represents the major public health problem worldwide nowadays, which has generated a high number of infections and deaths. The spike protein (S protein) is the most studied protein of SARS-CoV-2, and key to host-cell entry through ACE2 receptor. This protein presents a large pattern of glycosylations with important roles in immunity and infection mechanisms. Therefore, understanding key aspects of the molecular mechanisms of these structures, during drug recognition in SARS-CoV-2, may contribute to therapeutic alternatives. In this work, we explored the impact of glycosylations on the drug recognition on two domains of the S protein, the receptor-binding domain (RBD) and the N-terminal domain (NTD) through molecular dynamics simulations and computational biophysics analysis. Our results show that glycosylations in the S protein induce structural stability and changes in rigidity/flexibility related to the number of glycosylations in the structure. These structural changes are important for its biological activity as well as the correct interaction of ligands in the RBD and NTD regions. Additionally, we evidenced a roto-translation phenomenon in the interaction of the ligand with RBD in the absence of glycosylation, which disappears due to the influence of glycosylation and the convergence of metastable states in RBM. Similarly, glycosylations in NTD promote an induced fit phenomenon, which is not observed in the absence of glycosylations; this process is decisive for the activity of the ligand at the cryptic site. Altogether, these results provide an explanation of glycosylation relevance in biophysical properties and drug recognition to S protein of SARS-CoV-2, which must be considered in the rational drug development and virtual screening targeting S protein.

Exploiting Knowledge on Structure-Activity Relationships for Designing Peptidomimetics of Endogenous Peptides

Endogenous peptides are important mediators in cell communication, being consequently involved in many physiological processes. Their use as therapeutic agents is limited due to their poor pharmacokinetic profile. To circumvent this drawback, alternative diverse molecules based on the stereochemical features that confer their activity can be synthesized, using them as guidance; from peptide surrogates provided with a better pharmacokinetic profile, to small molecule peptidomimetics, through cyclic peptides. The design process requires a competent use of the structure-activity results available on individual peptides. Specifically, it requires synthesis and analysis of the activity of diverse analogs, biophysical information and computational work. In the present work, we show a general framework of the process and show its application to two specific examples: the design of selective AT1 antagonists of angiotensin and the design of selective B2 antagonists of bradykinin.