DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

Characterization and preparation of food-derived peptides on improving osteoporosis: A review

Osteoporosis is a systemic bone disease characterized by reduced bone mass and deterioration of the microstructure of bone tissue, leading to an increased risk of fragility fractures and affecting human health worldwide. Food-derived peptides are widely used in functional foods due to their low toxicity, ease of digestion and absorption, and potential to improve osteoporosis. This review summarized and discussed methods of diagnosing osteoporosis, treatment approaches, specific peptides as alternatives to conventional drugs, and the laboratory preparation and identification methods of peptides. It was found that peptides interacting with RGD (arginine-glycine-aspartic acid)-binding active sites in integrin could alleviate osteoporosis, analyzed the interaction sites between these osteogenic peptides and integrin, and further discussed their effects on improving osteoporosis. These may provide new insights for rapid screening of osteogenic peptides, and provide a theoretical basis for their application in bone materials and functional foods.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction

Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins, we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearson's R Spearman's Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings did not show a significant change in performance.

Identification of natural product-based effective inhibitors of spleen tyrosine kinase (SYK) through virtual screening and molecular dynamics simulation approaches

Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase that plays an essential role in signal transduction across different cell types. In the context of allergy and autoimmune disorders, it is a crucial regulator of immune receptor signaling in inflammatory cells such as B cells, mast cells, macrophages, and neutrophils. Developing SYK kinase inhibitors has gained significant interest for potential therapeutic applications in neurological and cancer-related conditions. The clinical use of the most advanced SYK inhibitor, Fostamatinib, has been limited due to its unwanted side effects. Thus, a more targeted approach to SYK inhibition would provide a more comprehensive treatment window. In this study, we used a virtual screening approach to identify potential SYK inhibitors from natural compounds from the IMPPAT database. We identified two compounds, Isolysergic acid and Michelanugine, which showed strong affinity and specificity for the SYK binding pocket. All-atom molecular dynamics (MD) simulations were also performed to explore the stability, conformational changes, and interaction mechanism of SYK in complexes with the identified compounds. The identified compounds might have the potential to be developed into promising SYK inhibitors for the treatment of various diseases, including autoimmune disorders, cancer, and inflammatory diseases. This work aims to identify potential phytochemicals to develop a new protein kinase inhibitor for treating advanced malignancies by providing an updated understanding of the role of SYK.Communicated by Ramaswamy H. Sarma.

Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein-ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models' decision-making processes and accurately compare the performance of models.

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.

Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools

Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.

Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.

Bioprospecting microbes and enzymes for the production of pterocarpans and coumestans

The isoflavonoid derivatives, pterocarpans and coumestans, are explored for multiple clinical applications as osteo-regenerative, neuroprotective and anti-cancer agents. The use of plant-based systems to produce isoflavonoid derivatives is limited due to cost, scalability, and sustainability constraints. Microbial cell factories overcome these limitations in which model organisms such as Saccharomyces cerevisiae offer an efficient platform to produce isoflavonoids. Bioprospecting microbes and enzymes can provide an array of tools to enhance the production of these molecules. Other microbes that naturally produce isoflavonoids present a novel alternative as production chassis and as a source of novel enzymes. Enzyme bioprospecting allows the complete identification of the pterocarpans and coumestans biosynthetic pathway, and the selection of the best enzymes based on activity and docking parameters. These enzymes consolidate an improved biosynthetic pathway for microbial-based production systems. In this review, we report the state-of-the-art for the production of key pterocarpans and coumestans, describing the enzymes already identified and the current gaps. We report available databases and tools for microbial bioprospecting to select the best production chassis. We propose the use of a holistic and multidisciplinary bioprospecting approach as the first step to identify the biosynthetic gaps, select the best microbial chassis, and increase productivity. We propose the use of microalgal species as microbial cell factories to produce pterocarpans and coumestans. The application of bioprospecting tools provides an exciting field to produce plant compounds such as isoflavonoid derivatives, efficiently and sustainably.

Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

A New Hybrid Neural Network Deep Learning Method for Protein-Ligand Binding Affinity Prediction and De Novo Drug Design

Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neural network) and the FFNN (fast forward neural network) hybrid neural network framework. The HNN-denovo uses protein pocket structure and protein-ligand interactions as input features. The HNN-affinity uses protein sequences and ligand features as input features. The HNN method combines the CNN and FCNN machine architecture for the protein structure or protein sequence and ligand descriptors. To train the model, the HNN methods used thousands of known protein-ligand binding affinity data retrieved from the PDBBind database. We also developed the Random Forest (RF), Gradient Boosting (GB), Decision Tree with AdaBoost (DT), and a consensus model. We compared the HNN results with models developed based on the RF, GB, and DT methods. We also independently compared the HNN method results with the literature reported deep learning protein-ligand binding affinity predictions made by the DLSCORE, KDEEP, and DeepAtom. The predictive performance of the HNN methods (max Pearson's R achieved was 0.86) was consistently better than or comparable to the DLSCORE, KDEEP, and DeepAtom deep learning learning methods for both balanced and unbalanced data sets. The HNN-affinity can be applied for the protein-ligand affinity prediction even in the absence of protein structure information, as it considers the protein sequence as standalone feature in addition to the ligand descriptors. The HNN-denovo method can be efficiently implemented to the structure-based de novo drug design campaign. The HNN-affinity method can be used in conjunction with the deep learning molecular docking protocols as a standalone. Further, it can be combined with the conventional molecular docking methods as a multistep approach to rapidly screen billions of diverse compounds. The HNN method are highly scalable in the cloud ML platform.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

A brief review of protein-ligand interaction prediction

The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in vitro experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process. In this review, we summarize a brief introduction to various computation-based PLIs. We discuss these approaches, in particular, machine learning-based methods, with illustrations of different emphases based on mainstream trends. Moreover, we analyzed three research dynamics that can be further explored in future studies.

On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks

Accurate prediction of binding affinities from protein-ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks describing both the ligand and the protein in their free and bound states, we unambiguously evidence that an explicit description of protein-ligand noncovalent interactions does not provide any advantage with respect to ligand or protein descriptors. Simple models, inferring binding affinities of test samples from that of the closest ligands or proteins in the training set, already exhibit good performances, suggesting that memorization largely dominates true learning in the deep neural networks. The current study suggests considering only noncovalent interactions while omitting their protein and ligand atomic environments. Removing all hidden biases probably requires much denser protein-ligand training matrices and a coordinated effort of the drug design community to solve the necessary protein-ligand structures.

Artificial intelligence in virtual screening: models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.

Cu(II)(PhOMe-Salophen) Complex: Greener Pasture Biological Study, XRD/HAS Interactions, and MEP

PhOMe-salophen (1b) (salophen is N,N-bis(salycilidene)-1,2-phenylenediamine with two tert-butyl on each ring) and Cu(II) complex with PhOMe-salophen (1c) have been synthesized and characterized using various tools, including X-ray diffraction for the Cu(II)-complex (1c, C₄₃H₅₂CuN₂O₃)). The copper complex has been obtained by Cu²⁺ templated approach using 1b. PhOMe-salophen (1b) has been obtained in reasonably high yield using a mixture of the Schiff-base, 1a, Pd(OAc)₂, PPh₃, Na₂CO₃, 4-methoxyphenylboronic acid in benzene. We focus in this research work on the electronic and structural properties of the Cu–Schiff base complex. The tetra-coordinate τ₄ index was calculated, indicating almost a perfect square planner in agreement with X-ray diffraction results. MEP reveals the maximum positive regions in 1/-associated with the azomethine and methoxyphenyl C–H bonds with an average value of 0.03 a.u. Hirshfeld surface analysis (HSA) was also studied to highlight the significant inter-atomic contacts and their percentage contribution through 2D Fingerprint plot. In a fair comparative molecular docking study, 1b and 1c were docked together with N-[{(5-methylisoxazol-3-yl)-carbonyl}alanyl}-l-valyl]-N1-((1R,2Z)-4-(benzyloxy)-4-oxo-1-[{(3R)-2-oxopyrrolidin-3-yl}methyl]but-2-enyl)-l-leucinamide, N3 against main protease M^pro, (PDB code 7BQY) using the same parameters and conditions. Interesting here to use the free energy, in silico, molecular docking approach, which aims to rank our molecules with respect to the well-known inhibitor, N3. The binding scores of 1b, 1c, N3 are –7.8, –9.0, and –8.4 kcal/mol, respectively. These preliminary results propose that ligands deserve additional study in the context of possible remedial agents for COVID-19.

In Vitro Investigation of Binding Interactions between Albumin–Gliclazide Model and Typical Hypotensive Drugs

Type 2 diabetes management usually requires polytherapy, which increases the risk of drug-to-drug interactions. Among the multiple diabetes comorbidities, hypertension is the most prevalent. This study aimed to investigate the binding interactions between the model protein, bovine albumin, and the hypoglycemic agent gliclazide (GLICL) in the presence of typical hypotensive drugs: quinapril hydrochloride (QUI), valsartan (VAL), furosemide (FUR), amlodipine besylate (AML), and atenolol (ATN). Spectroscopic techniques (fluorescence quenching, circular dichroism) and thermodynamic experiments were employed. The binding of the gliclazide to the albumin molecule was affected by the presence of an additional drug ligand, which was reflected by the reduced binding constant of the BSA–DRUG–GLICL system. This may indicate a possible GLICL displacement and its enhanced pharmacological effect, as manifested in clinical practice. The analysis of the thermodynamic parameters indicated the spontaneity of the reaction and emphasized the role of hydrogen bonding and van der Waals forces in these interactions. The secondary structure of the BSA remained almost unaffected.