Development of an effective method for purifying trypsin using a recombinant inhibitor.

A trypsin affinity material was prepared by covalently immobilizing buckwheat trypsin inhibitor (BTI) on epichlorohydrin-activated cross-linked agarose gel (Selfinose CL 6 B). The optimal conditions for activating Selfinose CL 6 B were 15 % epichlorohydrin and 0.8 M NaOH at 40 °C for 2 h. The optimal pH for immobilizing BTI was 9.5. BTI-Sefinose CL 6 B showed a maximum adsorption capacity of 2.25 mg trypsin/(g support). The material also displayed good reusability, retaining over 90 % of its initial adsorption capacity after 30 cycles. High-purity trypsin was obtained from locust homogenate using BTI-Selfinose CL 6 B through one-step affinity chromatography. The molecular mass and Km value of locust trypsin were determined as 27 kDa and 0.241 mM using N-benzoyl-DL-arginine-nitroanilide as substrate. The optimal temperature and pH of trypsin activity were 55 °C and 9.0, respectively. The enzyme exhibited good stability in the temperature range of 30-50 °C and pH range of 4.0-10.0. BTI-Selfinose CL 6 B demonstrates potential application in the preparation of high-purity trypsin and the discovery of more novel trypsin from various species.

Online ligand screening for cytochrome C from herbal medicines through "four-in-one" measurement.

Affinity-oriented online ligand screening with LC coupled to different detectors is widely popular to capture active compounds from herbal medicines (HMs). However, false-positive extensively occurs because insufficient information is recorded for the existence and stability of ligand-protein complex. Here, efforts were made to advance the hit confidences via configuring post-column infusion-LC-energy-resolved-affinity MS (PCI-LC-ER-AMS) to achieve "four-in-one" monitoring of: 1) response decrement of potential ligands; 2) response decrement of protein; 3) ions relating to ligand-protein complexes; and 4) ligand-protein binding strength. Ligand fishing for Cyt C from HMs was conducted as a proof-of-concept. For utility justification, a mimic sample containing twelve well-defined ligands and two negative controls underwent LC separation and met Cyt C prior to Qtof-MS measurements. Compared to Cyt C- or ligand-free assay, twelve ligands instead of negative controls showed response decrements that were consistent with twelve negative peaks observed at retention times corresponding to the ligands in Cyt C ion current chromatogram. Serial ions correlating to each ligand-Cyt C complex were observed. After recording breakdown graphs, optimal collision energy (OCE) corresponding to the non-covalent bond dissociation was positively correlated with binding strength. Two HMs including Scutellariae Radix (SR) and Aconiti Lateralis Radix Preparata were investigated. Consequently, 24 compounds were merely fished from SR, and particularly, flavonoid glycosides exhibited greater OCEs and also binding strengths over aglycones. Affinity assays and cellular evaluations consolidated the significant interactions between each captured compound and Cyt C. Overall, PCI-LC-ER-AMS is eligible for confidence-enhanced online ligand screening for Cyt C from HMs through "four-in-one" measurement.

Recurrent SARS-CoV-2 spike mutations confer growth advantages to select JN.1 sublineages.

The recently dominant SARS-CoV-2 Omicron JN.1 has evolved into multiple sublineages, with recurrent spike mutations R346T, F456L, and T572I, some of which exhibit growth advantages, such as KP.2 and KP.3. We investigated these mutations in JN.1, examining their individual and combined effects on immune evasion, ACE2 receptor affinity, and in vitro infectivity. F456L increased resistance to neutralization by human sera, including those after JN.1 breakthrough infections, and by RBD class-1 monoclonal antibodies, significantly altering JN.1 antigenicity. R346T enhanced ACE2-binding affinity and modestly boosted the infectivity of JN.1 pseudovirus, without a discernible effect on serum neutralization, while T572I slightly bolstered evasion of SD1-directed mAbs against JN.1's ancestor, BA.2, possibly by altering SD1 conformation. Importantly, expanding sublineages such as KP.2 containing R346T, F456L, and V1104L, showed similar neutralization resistance as JN.1 with R346T and F456L, suggesting V1104L does not appreciably affect antibody evasion. Furthermore, the hallmark mutation Q493E in KP.3 significantly reduced ACE2-binding affinity and viral infectivity, without noticeably impacting serum neutralization. Our findings illustrate how certain JN.1 mutations confer growth advantages in the population and could inform the design of the next COVID-19 vaccine booster.

Target-specific affinity separation of the bioactive compounds from herbal extract using the spin column packed with the immobilized protein microspheres prior to LC-MS analysis.

Excellent pretreatments before instrumental analysis are critical for separation and determination of target compounds for discovery of new drugs from herb medicines. We developed a rapid and highly-selective method to separate the bioactive compounds from herbal extract using protein affinity-selection spin column, which was packed with the new sorbent materials from integrating the recombinant ß2-adrenoceptor (ß2-AR) directly out of cell lysates onto the surface of microspheres. Protein affinity-selection spin column was placed in a centrifugal tube, where after the non-specific binders were released to the filtrate under the operational centrifugation, the specific binders on the spin column were cleaned with a washing solvent for LC-MS analysis. The known agonists of ß2-AR were retained/released on protein affinity-selection spin column but not on control column, demonstrating the method with good recovery (79.4∼95.7 %) and high repeatability (RSD < 3.5 %). The adsorption features of three ligands on the spin column were described best by Prism saturation binding model, and the high-affinity binding and the large binding capacity of the spin column make it feasible to trap the trace analytes effectively. It was applied in separating bioactive compounds from Alstoniae Scholaris extract, two of which were identified as picrinine and oleanolic acid in combination with LC-MS and verified as the potential agonists towards ß2-AR though molecular docking and cell experiments. Our study demonstrated that, the spin column with the immobilized protein sorbents in the centrifugal filter device represents a promising tool, enabling rapid and target-specific affinity separation of the bioactive compounds from herbal extract.

Serotype-agnostic affinity purification of adeno-associated virus (AAV) via peptide-functionalized chromatographic resins.

Adeno-associated viruses (AAVs) have emerged as a prominent family of vectors for gene delivery, providing therapeutic options to diseases once deemed incurable. At the same time, they necessitate efficient and affordable purification methods that can be platformed to serve all AAV serotypes. Current chromatographic tools, while affording high product purity, fail to bind certain serotypes, provide limited yield and lifetime, and impose harsh elution conditions that can compromise the vector's activity and safety. Addressing these challenges, this work demonstrates the application of new peptide ligands as the first serotype-agnostic technology for AAV purification by affinity chromatography. Our study reveals a pH-dependent affinity interaction: AAV2, AAV3, AAV6, AAV9, and AAVrh.10 are effectively captured at neutral pH, while binding AAV1, AAV5, AAV7, and AAV8 is stronger in a slightly acidic environment. The elution of bound AAVs was achieved using magnesium chloride at neutral pH for all serotypes, consistently affording capsid yields above 50% and genome yields above 80%, together with a >100-fold reduction in host cell proteins and nucleic acids. In particular, peptide ligand A10 exhibited remarkable binding capacity (> 1014 vp per mL of resin) and purification performance for all AAV serotypes, demonstrating broad applicability for gene therapy manufacturing. Finally, this work introduces novel alkaline-stable variants of A10 and demonstrates their use as the first affinity ligands capable of performing multiple cycles of AAV2, AAV8, and AAV9 purification with intermediate caustic cleaning without loss of capacity or product quality. Collectively, these results demonstrate the promise of this technology to further the impact and affordability of gene therapy.

Resistance-based directed evolution of nanobodies for higher affinity in prokaryotes.

A prokaryotic resistance-based directed evolution system leveraging protein-fragment complementation assay (PCA) was devised, and its proficiency in detecting protein-protein interactions and discriminating varying degrees of binding affinity was demonstrated by two well-characterized protein pairs. Furthermore, we constructed a random mutant library based on the GBPR36K/E45K mutant, characterized by almost no affinity towards EGFP. This library was subjected to PCA-based prokaryotic directed evolution, resulting in the isolation of back-mutated variants. In summary, we have established an expedited, cost-effective, and structural information-independent PCA-based prokaryotic directed evolution platform for nanobody affinity maturation, featuring tunable screening stringency via modulation of antibiotic concentrations.

Decoding the mannose receptor-mAb interaction: the importance of high-mannose N-glycans and glycan-pairing.

During the development process of therapeutic monoclonal antibodies (mAbs), it is crucial to control (critical) quality attributes such as N-glycosylation influencing pharmacokinetics (PK) and Fc effector functions. Previous reports have shown that mAbs containing high-mannose N-glycans are cleared faster from blood circulation, leading to reduced half-lives. The high-mannose N-glycan content of mAbs can be influenced during the cell culture process by factors such as cell lines, process conditions, and media. Furthermore, mAbs have either one high mannose N-glycan (asymmetrical high-mannose glyco-pair) or two high mannose N-glycans (symmetrical high-mannose glyco-pair). The hypothesis that the mannose receptor (MR, CD206) accelerates clearance by facilitating their internalization and subsequent lysosomal degradation is widespread. However, the interaction between MR and mAbs has not been explicitly demonstrated. This study aimed to investigate this interaction, providing the first systematic demonstration of MR binding to the Fc region of mAbs with high-mannose N-glycans. Two novel analytical methods, MR surface plasmon resonance and MR affinity chromatography, were developed and applied to investigate the MR-mAb interaction. The interaction is found to be dependent on high-mannose content, but is independent of the mAb format or sequence. However, different glyco-pairs exhibited varying binding affinities to the MR, with the symmetrical high-mannose glyco-pair showing the strongest binding properties. These findings strengthen the hypothesis for the MR-mediated mAb interaction and contribute to a deeper understanding of the MR-mAb interaction, which could affect the criticality of high-mannose containing mAbs development strategies of IgG-based molecules and improve their PK profiles.

Design, synthesis, and in vitro biological evaluation of meta-sulfonamidobenzamide-based antibacterial LpxH inhibitors.

New antibacterial compounds are urgently needed, especially for infections caused by the top-priority Gram-negative bacteria that are increasingly difficult to treat. Lipid A is a key component of the Gram-negative outer membrane and the LpxH enzyme plays an important role in its biosynthesis, making it a promising antibacterial target. Inspired by previously reported ortho-N-methyl-sulfonamidobenzamide-based LpxH inhibitors, novel benzamide substitutions were explored in this work to assess their in vitro activity. Our findings reveal that maintaining wild-type antibacterial activity necessitates removal of the N-methyl group when shifting the ortho-N-methyl-sulfonamide to the meta-position. This discovery led to the synthesis of meta-sulfonamidobenzamide analogs with potent antibacterial activity and enzyme inhibition. Moreover, we demonstrate that modifying the benzamide scaffold can alter blocking of the cardiac voltage-gated potassium ion channel hERG. Furthermore, two LpxH-bound X-ray structures show how the enzyme-ligand interactions of the meta-sulfonamidobenzamide analogs differ from those of the previously reported ortho analogs. Overall, our study has identified meta-sulfonamidobenzamide derivatives as promising LpxH inhibitors with the potential for optimization in future antibacterial hit-to-lead programs.

Screening of anti-inflammatory active components in Sabia schumanniana Diels by affinity ultrafiltration and UHPLC-Q-Exactive Orbitrap mass spectrometry.

ETHNOPHARMACOLOGICAL RELEVANCE: Sabia schumanniana Diels is a traditional botanical used to treat lumbago and arthralgia. However, there has been limited research on the pharmacological effects of its chemical components. AIM OF THE STUDY: This study aimed to rapidly screen for anti-inflammatory compounds in Sabia schumanniana Diels. MATERIALS AND METHODS: An affinity ultrafiltration method based on UHPLC-Q-Exactive Orbitrap MS was established to rapidly screen and identify cyclooxygenase-2 (COX-2) receptor ligands. The reliability of this method was verified by molecular docking analysis and experiments with RAW264.7 cells. RESULTS: Seventeen ligands were identified from Sabia schumanniana Diels using affinity ultrafiltration. Molecular docking results indicated that these ligands specifically docked with COX-2. Among them, N-nornuciferine exhibited notable anti-inflammatory activity. CONCLUSIONS: The combination of affinity ultrafiltration and UHPLC-Q-Exactive Orbitrap MS is an effective and precise method for screening anti-inflammatory compounds. This study provides a foundation for further research on Sabia schumanniana Diels and offers guidance for its potential clinical applications.

SSR-DTA: Substructure-aware multi-layer graph neural networks for drug-target binding affinity prediction.

Accurate prediction of drug-target binding affinity (DTA) is essential in the field of drug discovery. Recently, scientists have been attempting to utilize artificial intelligence prediction to screen out a significant number of ineffective compounds, thereby mitigating labor and financial losses. While graph neural networks (GNNs) have been applied to DTA, existing GNNs have limitations in effectively extracting substructural features across various sizes. Functional groups play a crucial role in modulating molecular properties, but existing GNNs struggle with feature extraction from certain motifs due to scale mismatches. Additionally, sequence-based models for target proteins lack the integration of structural information. To address these limitations, we present SSR-DTA, a multi-layer graph network capable of adapting to diverse structural sizes, which can extract richer biological features, thereby improving the robustness and accuracy of predictions. Multi-layer GNNs enable the capture of molecular motifs across different scales, ranging from atomic to macrocyclic motifs. Furthermore, we introduce BiGNN to simultaneously learn sequence and structural information. Sequence information corresponds to the primary structure of proteins, while graph information represents the tertiary structure. BiGNN assimilates richer information compared to sequence-based methods while mitigating the impact of errors from predicted structures, resulting in more accurate predictions. Through rigorous experimental evaluations conducted on four benchmark datasets, we demonstrate the superiority of SSR-DTA over state-of-the-art models. Particularly, in comparison to state-of-the-art models, SSR-DTA demonstrates an impressive 20% reduction in mean squared error on the Davis dataset and a 5% reduction on the KIBA dataset, underscoring its potential as a valuable tool for advancing DTA prediction.

Role of MARK4 in methamphetamine-induced acute kidney injury. / MARK4在甲基苯丙胺诱导急性肾损伤中的作用.

OBJECTIVES: Methamphetamine (METH) is an illicit psychoactive substance that can damage various organs, with the urinary system being one of its significant targets. This study aims to explore the role of microtubule affinity-regulating kinase 4 (MARK4) in METH-induced acute kidney injury (AKI). METHODS: A total of 10 healthy adult male C57BL/6 mice were randomly divided into a control group and a METH group, 5 mice in each group. The METH group was administered METH (20 mg/kg, intraperitoneally, once daily for 3 consecutive days), while the control group received an equal volume of physiological saline. The mice were executed 24 hours after the final injection, and the success of the AKI model was detected by blood serum creatinine, blood urea nitrogen, and renal HE staining. Proteins differentially expressed between kidney tissues with METH-induced AKI and normal kidney tissues were screened by proteomics techniques and subjected to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and bioinformatics analysis. The accuracy of proteomic data was validated using Western blotting, and the expression levels of MARK4 and cleaved caspase-3 in mouse kidneys were measured. We further explored the role of MARK4 in METH-induced AKI. Firstly, a METH toxicity model was established in BUMPT cells to screen the appropriate concentration and time of METH treatment; the viability of BUMPT cells after METH treatment and the expression of cleaved caspase-3 were detected by interfering with MARK4 expression through inhibitors. RESULTS: The proteomic analysis of kidney tissues from METH and control groups screened for a total of 17 differentially expressed proteins, of which 11 were up-regulated and 6 were down-regulated (all P<0.05). The expression levels of MARK4 and cleaved caspase-3 were elevated in the kidneys of METH-treated mice (both P<0.05). The activity of BUMPT cells gradually decreased with increasing METH treatment concentration (all P<0.05), where the viability of BUMPT cells decreased to about 60% after METH treatment at 4 mmol/L. Compared with the control group, expression levels of MARK4 and cleaved caspase-3 were increased with higher METH concentrations and longer exposure times in a concentration- and time-dependent manner (all P<0.05). Inhibition of MARK4 expression improved METH-induced decrease in BUMPT cell activity, down-regulated the expression of cleaved caspase-3, and decreased the apoptosis of BUMPT cells induced by METH. CONCLUSIONS: MARK4 is highly expressed in a mouse model of METH-induced AKI, and MARK4 mediates METH-induced AKI by regulating cell apoptosis.

Discovery of ICOS-targeted small molecules using affinity selection mass spectrometry screening.

Inducible T cell co-stimulator (ICOS) is a positive immune checkpoint receptor expressed on the surface of activated T cells, which could promote cell function after being stimulated with ICOS ligand (ICOS-L). Although clinical benefits have been reported in the ICOS modulation-based treatment for cancer and autoimmune disease, current modulators are restricted in biologics, whereas ICOS-targeted small molecules are lacking. To fill this gap, we performed an affinity selection mass spectrometry (ASMS) screening for ICOS binding using a library of 15,600 molecules. To the best of our knowledge, this is the first study that utilizes ASMS screening to discover small molecules targeting immune checkpoints. Compound 9 with a promising ICOS/ICOS-L inhibitory profile (IC50 = 29.38 ± 3.41 µM) was selected as the template for the modification. Following preliminary structure-activity relationship (SAR) study and molecular dynamic (MD) simulation revealed the critical role of the ortho-hydroxy group on compound 9 in the ICOS binding, as it could stabilize the interaction via the hydrogen bond formation with residuals on the glycan, and the depletion could lead to an activity lost. This work validates a promising inhibitor for the ICOS/ICOS-L interaction, and we anticipate future modifications could provide more potent modulators for this interaction.

Engineering and physicochemical characterization of a novel, stable, symmetric bispecific antibody with dual target-binding using a common light chain.

Bispecific antibodies (BsAbs) have emerged as a major class of antibody therapeutics owing to their substantial potential in disease treatment. While several BsAbs have been successfully approved in recent years, ongoing development efforts continue to focus on optimizing various BsAbs tailored to particular antigens and action mechanisms, aiming to achieve favorable physicochemical properties. BsAbs generally encounter challenges due to their unfavorable physicochemical characteristics and poor manufacturing efficiencies, highlighting the need for optimization to achieve reliable productivity and developability. Herein, we describe the development of a novel symmetric BsAb, REGULGENT™ (N-term/C-term), comprising two Fab domains, using a common light chain. The heavy chain fragment encoded two antigen-binding determinants in one chain. The design and production of REGULGENT™ (N-term/C-term) are simple owing to the use of the same light chain, which does not induce heavy and light chain mispairing, frequently observed with the asymmetric BsAb format. REGULGENT™ (N-term/C-term) exhibited high expression and low aggregation characteristics during cell culture and stress treatment under low pH conditions. Differential scanning calorimetric data indicated that REGULGENT™ molecules had high conformational stability, similar to that of stabilized monoclonal antibodies. Surface plasmon resonance data showed that REGULGENT™ (N-term/C-term) could bind to two antigens simultaneously and exhibited a high affinity for two antigens. In summary, the symmetric BsAb format of REGULGENT™ confers its desirable IgG-like physicochemical properties, thus making it an excellent candidate for commercial development. The findings demonstrate a novel BsAb with substantial development potential for clinical applications.

PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity.

BACKGROUND: Mutations occurring in nucleic acids or proteins may affect the binding affinities of protein-nucleic acid interactions. Although many efforts have been devoted to the impact of protein mutations, few computational studies have addressed the effect of nucleic acid mutations and explored whether the identical methodology could be applied to the prediction of binding affinity changes caused by these two mutation types. RESULTS: Here, we developed a generalized algorithm named PNBACE for both DNA and protein mutations. We first demonstrated that DNA mutations could induce varying degrees of changes in binding affinity from multiple perspectives. We then designed a group of energy-based topological features based on different energy networks, which were combined with our previous partition-based energy features to construct individual prediction models through feature selections. Furthermore, we created an ensemble model by integrating the outputs of individual models using a differential evolution algorithm. In addition to predicting the impact of single-point mutations, PNBACE could predict the influence of multiple-point mutations and identify mutations significantly reducing binding affinities. Extensive comparisons indicated that PNBACE largely performed better than existing methods on both regression and classification tasks. CONCLUSIONS: PNBACE is an effective method for estimating the binding affinity changes of protein-nucleic acid complexes induced by DNA or protein mutations, therefore improving our understanding of the interactions between proteins and DNA/RNA.

An in vitro and machine learning framework for quantifying serum albumin binding of per- and polyfluoroalkyl substances.

Per- and polyfluoroalkyl substances (PFAS) are a diverse class of anthropogenic chemicals; many are persistent, bioaccumulative, and mobile in the environment. Worldwide, PFAS bioaccumulation causes serious adverse health impacts, yet the physiochemical determinants of bioaccumulation and toxicity for most PFAS are not well understood, largely due to experimental data deficiencies. As most PFAS are proteinophilic, protein binding is a critical parameter for predicting PFAS bioaccumulation and toxicity. Among these proteins, human serum albumin (HSA) is the predominant blood transport protein for many PFAS. We previously demonstrated the utility of an in vitro differential scanning fluorimetry assay for determining relative HSA binding affinities for 24 PFAS. Here, we report HSA affinities for 65 structurally diverse PFAS from 20 chemical classes. We leverage these experimental data, and chemical/molecular descriptors of PFAS, to build 7 machine learning classifier algorithms and 9 regression algorithms, and evaluate their performance to identify the best predictive binding models. Evaluation of model accuracy revealed that the top performing classifier model, logistic regression, had an AUROC statistic of 0.936. The top performing regression model, support vector regression, had an R2 of 0.854. These top performing models were then used to predict HSA-PFAS binding for chemicals in the EPAPFASINV list of 430 PFAS. These developed in vitro and in silico methodologies represent a high-throughput framework for predicting protein-PFAS binding based on empirical data, and generate directly comparable binding data of potential use in predictive modeling of PFAS bioaccumulation and other toxicokinetic endpoints.

Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aß2-7 Binding Affinity.

Seven treatments are approved for Alzheimer's disease, but five of them only relieve symptoms and do not alter the course of the disease. Aducanumab (Adu) and lecanemab are novel disease-modifying antiamyloid-ß (Aß) human monoclonal antibodies that specifically target the pathophysiology of Alzheimer's disease (AD) and were recently approved for its treatment. However, their administration is associated with serious side effects, and their use is limited to early stages of the disease. Therefore, drug discovery remains of great importance in AD research. To gain new insights into the development of novel drugs for Alzheimer's disease, a combination of techniques was employed, including mutation screening, molecular dynamics, and quantum biochemistry. These were used to outline the interfacial interactions of the Aducanumab::Aß2-7 complex. Our analysis identified critical stabilizing contacts, revealing up to 40% variation in the affinity of the Adu chains for Aß2-7 depending on the conformation outlined. Remarkably, two complementarity determining regions (CDRs) of the Adu heavy chain (HCDR3 and HCDR2) and one CDR of the Adu light chain (LCDR3) accounted for approximately 77% of the affinity of Adu for Aß2-7, confirming their critical role in epitope recognition. A single mutation, originally reported to have the potential to increase the affinity of Adu for Aß2-7, was shown to decrease its structural stability without increasing the overall binding affinity. Mimetic peptides that have the potential to inhibit Aß aggregation were designed by using computational outcomes. Our results support the use of these peptides as promising drugs with great potential as inhibitors of Aß aggregation.

Assessment of membrane-based downstream purification processes as a replacement to traditional resin bead for monoclonal antibody purification.

Membrane chromatography devices are a viable alternative to packed-bed resins and enable highly productive purification cascades for monoclonal antibodies and Fc-fusion proteins. In this study, ion exchange and protein A membrane chromatography performances were assessed and compared with their resin counterparts. Protein A dynamic binding capacities were higher than 50 g/L for two of the tested membranes and with a residence time of 0.2 min. For polishing, it was observed that aggregate clearance was generally less performant with membrane separation when compared to resins with similar ligands. However, the comparable yield and increased productivity of membranes could be enough to consider their implementation. In addition, lifetime studies demonstrated that the performance of membranes remained robust over cycles. One hundred cycles were reached for most of the tested membranes with no impact on the process performance nor product quality. Finally, purification cascades were fully operated with membranes, from capture to polishing, reaching good levels of host cells proteins (less than 50 ppm) and aggregates (equal to or less than 1%). The outcome of this study demonstrated that resin chromatography could be fully replaced by membranes for monoclonal antibody and Fc-fusion protein purification processes.

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

BACKGROUND: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. OBJECTIVE: This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction, providing a fresh perspective on this evolving field. METHODS: We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. RESULTS: The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. CONCLUSION: The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

Application of Silk Light Chain on a Graphene Composite Surface: A Molecular Dynamics and Electrochemical Experiment.

The surface functionalization of pristine graphene (PG) with beneficial biocomposites is important for biomedical and tissue engineering. This study introduces silk light chain as novel biocomposites to increase the biocompatibility of PG. We explored the supramolecular structures of the silk heavy and light chains. Through molecular dynamics, we compared and analyzed the structural effects and binding mechanisms of these domains in their interaction with PG. Our results highlighted a significant hydrophobic interaction between the silk light chain and PG, without structural collapse. The supramolecular structure of the silk light chain was identified by analyzing the amino acids bound to PG. Moreover, using the silk light chain, the hydrophobic surface of PG has changed to a hydrophilic surface, and the silk light-chain-PG electron transfer rate was evaluated for the graphene congeners: graphene oxide (GO) and reduced graphene oxide. Therefore, we are confident that the dispersibility and biocompatibility of PG can be increased using silk light chains, which will contribute to broadening the field of application of PG-based materials.