Structure-based Methods Research Articles

PON-Tm: A Sequence-Based Method for Prediction of Missense Mutation Effects on Protein Thermal Stability Changes.

Proteins, as crucial macromolecules performing diverse biological roles, are central to numerous biological processes. The ability to predict changes in protein thermal stability due to mutations is vital for both biomedical research and industrial applications. However, existing experimental methods are often costly and labor-intensive, while structure-based prediction methods demand significant computational resources. In this study, we introduce PON-Tm, a novel sequence-based method for predicting mutation-induced thermal stability variations in proteins. PON-Tm not only incorporates features predicted by a protein language model from protein sequences but also considers environmental factors such as pH and the thermostability of the wild-type protein. To evaluate the effectiveness of PON-Tm, we compared its performance to four well-established methods, and PON-Tm exhibited superior predictive capabilities. Furthermore, to facilitate easy access and utilization, we have developed a web server.

Computational Methods for the Discovery and Optimization of TAAR1 and TAAR5 Ligands.

G-protein-coupled receptors (GPCRs) represent a family of druggable targets when treating several diseases and continue to be a leading part of the drug discovery process. Trace amine-associated receptors (TAARs) are GPCRs involved in many physiological functions with TAAR1 having important roles within the central nervous system (CNS). By using homology modeling methods, the responsiveness of TAAR1 to endogenous and synthetic ligands has been explored. In addition, the discovery of different chemo-types as selective murine and/or human TAAR1 ligands has helped in the understanding of the species-specificity preferences. The availability of TAAR1-ligand complexes sheds light on how different ligands bind TAAR1. TAAR5 is considered an olfactory receptor but has specific involvement in some brain functions. In this case, the drug discovery effort has been limited. Here, we review the successful computational efforts developed in the search for novel TAAR1 and TAAR5 ligands. A specific focus on applying structure-based and/or ligand-based methods has been done. We also give a perspective of the experimental data available to guide the future drug design of new ligands, probing species-specificity preferences towards more selective ligands. Hints for applying repositioning approaches are also discussed.

Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes.

Capacitive storage devices allow for fast charge and discharge cycles, making them the perfect complements to batteries for high power applications. Many materials display interesting capacitive properties when they are put in contact with ionic solutions despite their very different structures and (surface) reactivity. Among them, nanocarbons are the most important for practical applications, but many nanomaterials have recently emerged, such as conductive metal-organic frameworks, 2D materials, and a wide variety of metal oxides. These heterogeneous and complex electrode materials are difficult to model with conventional approaches. However, the development of computational methods, the incorporation of machine learning techniques, and the increasing power in high performance computing now allow us to tackle these types of systems. In this Review, we summarize the current efforts in this direction. We show that depending on the nature of the materials and of the charging mechanisms, different methods, or combinations of them, can provide desirable atomic-scale insight on the interactions at play. We mainly focus on two important aspects: (i) the study of ion adsorption in complex nanoporous materials, which require the extension of constant potential molecular dynamics to multicomponent systems, and (ii) the characterization of Faradaic processes in pseudocapacitors, that involves the use of electronic structure-based methods. We also discuss how recently developed simulation methods will allow bridges to be made between double-layer capacitors and pseudocapacitors for future high power electricity storage devices.

Advances in bioinformatics-based protein function prediction

With the increasing of computer power and rapid expansion of biological data, the application of bioinformatics tools has become the mainstream approach to address biological problems. The accurate identification of protein function by bioinformatics tools is crucial for both biomedical research and drug discovery, making it a hot topic of research. In this paper, we categorize bioinformatics-based protein function prediction methods into three categories: protein sequence-based methods, protein structure-based methods, and protein interaction networks-based methods. We further analyze these specific algorithms, highlighting the latest research advancements and providing valuable references for the application of bioinformatics-based protein function prediction in biomedical research and drug discovery.

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics.

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen.

T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Discovery and characterization of small-molecule TGR5 ligands with agonistic activity

The Takeda G protein-coupled receptor 5 (TGR5) is activated endogenously by primary and secondary bile acids. This receptor is considered a candidate target for addressing inflammatory and metabolic disorders. We have targeted TGR5 with structure-based methods for ligand finding using the recently solved experimental structures, as well as structures obtained from molecular dynamics simulations. Through addressing the orthosteric as well as a putative allosteric site, we identified agonists and positive allosteric modulators. While the predicted binding locations were not in line with their efficacy, our work contributes activating small-molecule ligands that we have thoroughly characterized in vitro.

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences, overlooking crucial information required for the three-dimensional (3-D) space binding process. In this study, we propose TransfIGN, a structure-based DL model that is inspired by our previously developed framework, Interaction Graph Network (IGN), and incorporates sequence information from transformers to predict the interactions between HLA-A*02:01 and antigen peptides. Our model, trained on a comprehensive data set containing 61,816 sequences with 9051 binding affinity labels and 56,848 eluted ligand labels, achieves an area under the curve (AUC) of 0.893 on the binary data set, better than state-of-the-art sequence-based models trained on larger data sets such as NetMHCpan4.1, ANN, and TransPHLA. Furthermore, when evaluated on the IEDB weekly benchmark data sets, our predictions (AUC = 0.816) are better than those of the recommended methods like the IEDB consensus (AUC = 0.795). Notably, the interaction weight matrices generated by our method highlight the strong interactions at specific positions within peptides, emphasizing the model's ability to provide physical interpretability. This capability to unveil binding mechanisms through intricate structural features holds promise for new immunotherapeutic avenues.

Designing Small Molecule PI3Kγ Inhibitors: A Review of Structure-Based Methods and Computational Approaches.

The PI3K/AKT/mTOR pathway plays critical roles in a wide array of biological processes. Phosphatidylinositol 3-kinase gamma (PI3Kγ), a class IB PI3K family member, represents a potential therapeutic opportunity for the treatment of cancer, inflammation, and autoimmunity. In this Perspective, we provide a comprehensive overview of the structure, biological function, and regulation of PI3Kγ. We also focus on the development of PI3Kγ inhibitors over the past decade and emphasize their binding modes, structure-activity relationships, and pharmacological activities. The application of computational technologies and artificial intelligence in the discovery of novel PI3Kγ inhibitors is also introduced. This review aims to provide a timely and updated overview on the strategies for targeting PI3Kγ.

RBD design increases the functional antibody titers elicited by SARS-CoV-2 spike vaccination

Most COVID-19 vaccines contain the SARS-CoV-2 spike protein as an antigen, but they lose efficacy as neutralizing antibody titers wane and escape variants emerge. Modifying the spike antigen to increase neutralizing antibody titers would help counteract this decrease in titer. We previously used a structure-based computational design method to identify nine amino acid changes in the receptor-binding domain (RBD) of spike that stabilize the RBD and increase the neutralizing antibody titers elicited by vaccination. Here, we introduce those enhancing amino acid changes into a full-length spike (FL-S-2P) ectodomain representative of most approved vaccine antigens. These amino acid changes can be incorporated into the FL-S-2P protein without negatively effecting expression or stability. Furthermore, the amino acid changes improved functional antibody titers in both mice and monkeys following vaccination. These amino acid changes could increase the duration of protection conferred by most COVID-19 vaccines.

DP-site: A dual deep learning-based method for protein-peptide interaction site prediction

BackgroundProtein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the years, studies have shown that experimental methods have improved the identification of this bio-molecular interaction. However, predicting protein-peptide interactions using these methods is laborious, time-consuming, dependent on third-party tools, and costly. MethodTo address these previous drawbacks, this study introduces a computational framework called DP-Site. The proposed framework concentrates on using a compound of a dual pipeline along with a combination predictor. A deep convolutional neural network for feature extraction and classification is embedded in pipeline 1. In addition, pipeline 2 includes a deep long-short-term memory-based and a random forest classifier for feature extraction and classification. In this investigation, the evolutionary, structure-based, sequence-based, and physicochemical information of proteins is utilized for identifying protein-peptide interaction at the residue level. ResultsThe proposed method is evaluated on both the ten-fold cross-validation and independent test sets. The robust and consistent results between cross-validation and independent test sets confirm the ability of the proposed method to predict peptide binding residues in proteins. Moreover, experimental findings demonstrate that DP-Site has significantly outperformed other state-of-the-art sequence-based and structure-based methods. The proposed method achieves a remarkable balance between a specificity of 0.799 and a sensitivity of 0.770, along with the best f-measure of 0.661 and the highest precision of 0.580 using an independent test set. ConclusionsThe outcome of various experiments confirms the proficiency of the proposed method and outperforms state-of-the-art sequence-based and structure-based methods in terms of the mentioned criteria. DP-Site can be accessed at https://github.com/shafiee 95/shima.shafiee.DP-Site.

Structure-based virtual screening methods for the identification of novel phytochemical inhibitors targeting furin protease for the management of COVID-19.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is a highly contagious respiratory disease with widespread societal impact. The symptoms range from cough, fever, and pneumonia to complications affecting various organs, including the heart, kidneys, and nervous system. Despite various ongoing efforts, no effective drug has been developed to stop the spread of the virus. Although various types of medications used to treat bacterial and viral diseases have previously been employed to treat COVID-19 patients, their side effects have also been observed. The way SARS-CoV-2 infects the human body is very specific, as its spike protein plays an important role. The S subunit of virus spike protein cleaved by human proteases, such as furin protein, is an initial and important step for its internalization into a human host. Keeping this context, we attempted to inhibit the furin using phytochemicals that could produce minimal side effects. For this, we screened 408 natural phytochemicals from various plants having antiviral properties, against furin protein, and molecular docking and dynamics simulations were performed. Based on the binding score, the top three compounds (robustaflavone, withanolide, and amentoflavone) were selected for further validation. MM/GBSA energy calculations revealed that withanolide has the lowest binding energy of -57.2 kcal/mol followed by robustaflavone and amentoflavone with a binding energy of -45.2 kcal/mol and -39.68 kcal/mol, respectively. Additionally, ADME analysis showed drug-like properties for these three lead compounds. Hence, these natural compounds robustaflavone, withanolide, and amentoflavone, may have therapeutic potential for the management of SARS-CoV-2 by targeting furin.

A novel model for relation prediction in knowledge graphs exploiting semantic and structural feature integration

Relation prediction is a critical task in knowledge graph completion and associated downstream tasks that rely on knowledge representation. Previous studies indicate that both structural features and semantic information are meaningful for predicting missing relations in knowledge graphs. This has led to the development of two types of methods: structure-based methods and semantics-based methods. Since these two approaches represent two distinct learning paradigms, it is difficult to fully utilize both sets of features within a single learning model, especially deep features. As a result, existing studies usually focus on only one type of feature. This leads to an insufficient representation of knowledge in current methods and makes them prone to overlooking certain patterns when predicting missing relations. In this study, we introduce a novel model, RP-ISS, which combines deep semantic and structural features for relation prediction. The RP-ISS model utilizes a two-part architecture, with the first component being a RoBERTa module that is responsible for extracting semantic features from entity nodes. The second part of the system employs an edge-based relational message-passing network designed to capture and interpret structural information within the data. To alleviate the computational burden of the message-passing network on the RoBERTa module during the sampling process, RP-ISS introduces a node embedding memory bank, which updates asynchronously to circumvent excessive computation. The model was assessed on three publicly accessible datasets (WN18RR, WN18, and FB15k-237), and the results revealed that RP-ISS surpasses all baseline methods across all evaluation metrics. Moreover, RP-ISS showcases robust performance in graph inductive learning.

Accelerating Molecular Docking using Machine Learning Methods.

Virtual screening (VS) is one of the well-established approaches in drug discovery which speeds up the search for a bioactive molecule and, reduces costs and efforts associated with experiments. VS helps to narrow down the search space of chemical space and allows selecting fewer and more probable candidate compounds for experimental testing. Docking calculations are one of the commonly used and highly appreciated structure-based drug discovery methods. Databases for chemical structures of small molecules have been growing rapidly. However, at the moment virtual screening of large libraries via docking is not very common. In this work, we aim to accelerate docking studies by predicting docking scores without explicitly performing docking calculations. We experimented with an attention based long short-term memory (LSTM) neural network for an efficient prediction of docking scores as well as other machine learning models such as XGBoost. By using docking scores of a small number of ligands we trained our models and predicted docking scores of a few million molecules. Specifically, we tested our approaches on 11 datasets that were produced from in-house drug discovery studies. On average, by training models using only 7000 molecules we predicted docking scores of approximately 3.8 million molecules with R2 (coefficient of determination) of 0.77 and Spearman rank correlation coefficient of 0.85. We designed the system with ease of use in mind. All the user needs to provide is a csv file containing SMILES and their respective docking scores, the system then outputs a model that the user can use for the prediction of docking score for a new molecule.

Predicted mechanistic impacts of human protein missense variants.

Genome sequencing efforts have led to the discovery of tens of millions of protein missense variants found in the human population with the majority of these having no annotated role and some likely contributing to trait variation and disease. Sequence-based artificial intelligence approaches have become highly accurate at predicting variants that are detrimental to the function of proteins but they do not inform on mechanisms of disruption. Here we combined sequence and structure-based methods to perform proteome-wide prediction of deleterious variants with information on their impact on protein stability, protein-protein interactions and small-molecule binding pockets. AlphaFold2 structures were used to predict approximately 100,000 small-molecule binding pockets and stability changes for over 200 million variants. To inform on protein-protein interfaces we used AlphaFold2 to predict structures for nearly 500,000 protein complexes. We illustrate the value of mechanism-aware variant effect predictions to study the relation between protein stability and abundance and the structural properties of interfaces underlying trans protein quantitative trait loci (pQTLs). We characterised the distribution of mechanistic impacts of protein variants found in patients and experimentally studied example disease linked variants in FGFR1.

A comprehensive review and comparison of existing computational methods for protein function prediction.

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

Identification of TonB-dependent siderophore receptor inhibitors against Flavobacterium columnare using a structure-based high-throughput virtual screening method.

TonB-dependent siderophore receptors play a critical transport role for Flavobacterium columnare virulence formation and growth, and have become valuable targets for the development of novel antimicrobial agents. Traditional Chinese medicine has demonstrated notable efficacy in the treatment of fish diseases and includes potential antibacterial agents. Herein, we performed molecular docking-based virtual screening to discover novel TonB-dependent siderophore receptor inhibitors from traditional Chinese medicine and provide information for developing novel antibacterial agents. Firstly, we efficiently obtained 11 potential inhibitors with desirable drug-like characteristics from thousands of compounds in the TCM library based on virtual screening and property prediction. The antibacterial activity of Enoxolone, along with its interaction characteristics, were determined via an MIC assay and molecular dynamic simulation. Transcriptional profiling, along with validation experiments, subsequently revealed that an insufficient uptake of iron ions by bacteria upon binding to the TonB-dependent siderophore receptors is the antibacterial mechanism of Enoxolone. Finally, Enoxolone's acceptable toxicity was illustrated through immersion experiments. In summary, we have used virtual screening techniques for the first time in the development of antimicrobial agents in aquaculture. Through this process, we have identified Enoxolone as a promising compound targeting the TonB-dependent siderophore receptor of F. columnare. In addition, our findings will provide new ideas for the advancement of innovative antimicrobial medications in aquaculture.

Utilizing a structure-based virtual screening approach to discover potential LSD1 inhibitors

BackgroundLysine-specific demethylase 1 (LSD1) is highly expressed in a variety of malignant tumors, rendering it a crucial epigenetic target for anti-tumor therapy. Therefore, the inhibition of LSD1 activity has emerged as a promising innovative therapeutic approach for targeted cancer treatment.MethodsIn our study, we employed innovative structure-based drug design methods to meticulously select compounds from the ZINC15 database. Utilizing virtual docking, we evaluated docking scores and binding modes to identify potential inhibitors. To further validate our findings, we harnessed molecular dynamic simulations and conducted meticulous biochemical experiments to deeply analyze the binding interactions between the protein and compounds.ResultsOur results showcased that ZINC10039815 exhibits an exquisite binding mode with LSD1, fitting perfectly into the active pocket and forming robust interactions with multiple critical residues of the protein.ConclusionsWith its significant inhibitory effect on LSD1 activity, ZINC10039815 emerges as a highly promising candidate for the development of novel LSD1 inhibitors.

To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches

Auxins are chemical compounds of wide interest, mostly due to their role in plant metabolism and development. Synthetic auxins have been used as herbicides for more than 75 years and low toxicity in humans is one of their most advantageous features. Extensive studies of natural and synthetic auxins have been made in an effort to understand their role in plant growth. However, molecular details of the binding and recognition process are still an open question. Herein, we present a comprehensive in silico pipeline for the assessment of TIR1 ligands using several structure-based methods. Our results suggest that subtle dynamics within the binding pocket arise from water–ligand interactions. We also show that this trait distinguishes effective binders. Finally, we construct a database of putative ligands and decoy compounds, which can aid further studies focusing on synthetic auxin design. To the best of our knowledge, this study is the first of its kind focusing on TIR1.