Structure-based Classification Research Articles

Bridging the Gap between Sequence and Structure Classifications of Proteins with AlphaFold Models

Classification of protein domains based on homology and structural similarity serves as a fundamental tool to gain biological insights into protein function. Recent advancements in protein structure prediction, exemplified by AlphaFold, have revolutionized the availability of protein structural data. We focus on classifying about 9000 Pfam families into ECOD (Evolutionary Classification of Domains) by using predicted AlphaFold models and the DPAM (Domain Parser for AlphaFold Models) tool. Our results offer insights into their homologous relationships and domain boundaries. More than half of these Pfam families contain DPAM domains that can be confidently assigned to the ECOD hierarchy. Most assigned domains belong to highly populated folds such as Immunoglobulin-like (IgL), Armadillo (ARM), helix-turn-helix (HTH), and Src homology 3 (SH3). A large fraction of DPAM domains, however, cannot be confidently assigned to ECOD homologous groups. These unassigned domains exhibit statistically different characteristics, including shorter average length, fewer secondary structure elements, and more abundant transmembrane segments. They could potentially define novel families remotely related to domains with known structures or novel superfamilies and folds. Manual scrutiny of a subset of these domains revealed an abundance of internal duplications and recurring structural motifs. Exploring sequence and structural features such as disulfide bond patterns, metal-binding sites, and enzyme active sites helped uncover novel structural folds as well as remote evolutionary relationships. By bridging the gap between sequence-based Pfam and structure-based ECOD domain classifications, our study contributes to a more comprehensive understanding of the protein universe by providing structural and functional insights into previously uncharacterized proteins.

Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment

Per- and Polyfluoroalkyl substances (PFAS) are a class of manufactured chemicals that are in widespread use and many present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterized for their hazard profiles, the vast majority have not been extensively studied. Herein, a chemical category approach was developed and applied to PFAS that could be readily characterized by a chemical structure. The PFAS definition as described in the Toxic Substances Control Act (TSCA) section 8(a)(7) rule was applied to the Distributed Structure-Searchable Toxicity (DSSTox) database to retrieve an initial list of 13,054 PFAS. Plausible degradation products from the 563 PFAS on the non-confidential TSCA Inventory were simulated using the Catalogic expert system, and the unique predicted PFAS degradants (2484) that conformed to the same PFAS definition were added to the list resulting in a set of 15,538 PFAS. Each PFAS was then assigned into a primary category using Organisation for Economic Co-operation and Development (OECD) structure-based classifications. The primary categories were subdivided into secondary categories based on a chain length threshold (>=7 vs < 7). Secondary categories were subcategorized using chemical fingerprints to achieve a balance between total number of structural categories vs.level of structural similarity within a category based on the Jaccard index. A set of 128 terminal structural categories were derived from which a subset of representative candidates could be proposed for potential data collection, considering the sparsity of relevant toxicity data within each category, presence on environmental monitoring lists, and the ability to identify plausible manufacturers/importers. Refinements to the approach taking into consideration ways in which the categories could be updated by mechanistic data and physicochemical property information are also described. This categorization approach may be used to form the basis of identifying candidates for data collection with related applications in QSAR development, read-across and hazard assessment.

DrugDomain: The evolutionary context of drugs and small molecules bound to domains.

Interactions between proteins and small organic compounds play a crucial role in regulating protein functions. These interactions can modulate various aspects of protein behavior, including enzymatic activity, signaling cascades, and structural stability. By binding to specific sites on proteins, small organic compounds can induce conformational changes, alter protein-protein interactions, or directly affect catalytic activity. Therefore, many drugs available on the market today are small molecules (72% of all approved drugs in the last 5 years). Proteins are composed of one or more domains: evolutionary units that convey function or fitness either singly or in concert with others. Understanding which domain(s) of the target protein binds to a drug can lead to additional opportunities for discovering novel targets. The evolutionary classification of protein domains (ECOD) classifies domains into an evolutionary hierarchy that focuses on distant homology. Previously, no structure-based protein domain classification existed that included information about both the interaction between small molecules or drugs and the structural domains of a target protein. This data is especially important for multidomain proteins and large complexes. Here, we present the DrugDomain database that reports the interaction between ECOD of human target proteins and DrugBank molecules and drugs. The pilot version of DrugDomain describes the interaction of 5160 DrugBank molecules associated with 2573 human proteins. It describes domains for all experimentally determined structures of these proteins and incorporates AlphaFold models when such structures are unavailable. The DrugDomain database is available online: http://prodata.swmed.edu/DrugDomain/.

Structure-based classification and nomogram prediction for differential inhibitor sensitivity of EGFR exon 19 deletions.

Structure-based classification and nomogram prediction for differential inhibitor sensitivity of EGFR exon 19 deletions.

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.

Rare oncogenic alterations in NSCLC—focus on atypical EGFR mutations

SummaryIn non-small cell lung cancer (NSCLC), mutations within the epidermal growth factor receptor (EGFR) gene are very common driver mutations. EGFR tyrosine kinase inhibitors (TKIs), have shown remarkable efficacy in patients with common classical EGFR mutations (L858R and exon 19 deletions). However, the landscape becomes intricate with atypical (also known as uncommon) EGFR mutations, comprising up to 30% of cases. This brief review provides an insight into recent studies, shedding light on the challenges and opportunities posed by uncommon mutations. The exploration encompasses clinical outcomes, treatment responses, and innovative approaches such as structure-based classifications, offering insights into the evolving paradigm of precision medicine for NSCLC patients with diverse EGFR mutations.

A DFT study on the fundamental mechanisms of quantum capacitance enhancement within the carbon-based electrodes through different classes of doped configurations from biomass-derived elements

Density functional calculations were performed on 15 functionalized graphene models to investigate the enhancement of quantum capacitance (CQ) by common dopant elements N, P, S, and O from biomaterials. Geometry optimizations and formation energy calculations demonstrated that the van der Waals radius and additional covalent bonds influenced the mechanical stress and formation energy, particularly due to the distortion of the graphene lattice caused by larger S or P atoms replacing carbon atoms. According to both the CQ and formation energy calculations, nitrogen emerged as the most promising doping element for enhancing CQ, followed by phosphorus, while sulfur showed a relatively lower contribution. Electron density profiles indicated that the improvement of CQ was facilitated by the lone pair electrons at the defects. The effects of dopants on electronic structures were further elucidated through CQ characteristics, resulting in the classification of functionalized graphene models into three types. The ‘graphitic’ type represented configurations that preserved most of the electronic structure of pristine graphene, while ‘p-type’ and ‘n-type’ represented those experienced the loss and gain of valence electrons, respectively. This electronic structure-based classification of doping provides valuable insights for future designs, enabling control over sintering and doping conditions in biomass-derived electrode materials for supercapacitors.

Tau and neurodegeneration.

First identified in 1975, tau was implicated in Alzheimer's disease 10 years later. Filamentous tangle inclusions were known to be made of hyperphosphorylated tau by 1991, with similar inclusions gaining recognition for being associated with other neurodegenerative diseases. In 1998, mutations in MAPT, the gene that encodes tau, were identified as the cause of a dominantly inherited form of frontotemporal dementia with abundant filamentous tau inclusions. While this result indicated that assembly of tau into aberrant filaments is sufficient to drive neurodegeneration and dementia, most cases of tauopathy are sporadic. More recent work in experimental systems showed that filamentous assemblies of tau may first form in one brain area, and then spread to others in a prion-like fashion. Beginning in 2017, work on human brains using high-resolution techniques has led to a structure-based classification of tauopathies, which has opened the door to a better understanding of the significance of tau filament formation.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put into 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-AadharID, for each of these 149,169,443 molecules. MPDS-AadharID is akin to the unique number given to citizens in India (similar to SSN in the US and NINO in the UK). The unique features of MPDS-CL are (a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; (b) automatic generation of information for the processing for MPDS and other galaxy tools; (c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and (d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at https://mpds.neist.res.in:8086/ .

Predicted structural proteome of Sphagnum divinum and proteome-scale annotation.

Sphagnum-dominated peatlands store a substantial amount of terrestrial carbon. The genus is undersampled and under-studied. No experimental crystal structure from any Sphagnum species exists in the Protein Data Bank and fewer than 200 Sphagnum-related genes have structural models available in the AlphaFold Protein Structure Database. Tools and resources are needed to help bridge these gaps, and to enable the analysis of other structural proteomes now made possible by accurate structure prediction. We present the predicted structural proteome (25134 primary transcripts) of Sphagnum divinum computed using AlphaFold, structural alignment results of all high-confidence models against an annotated nonredundant crystallographic database of over 90,000 structures, a structure-based classification of putative Enzyme Commission (EC) numbers across this proteome, and the computational method to perform this proteome-scale structure-based annotation. All data and code are available in public repositories, detailed at https://github.com/BSDExabio/SAFA. The structural models of the S. divinum proteome have been deposited in the ModelArchive repository at https://modelarchive.org/doi/10.5452/ma-ornl-sphdiv.

Exploring Influences of Leaves on Urban Species Identification Using Handheld Laser Scanning Point Cloud: A Case Study in Hong Kong

Urban tree species identification performs a significant role in tree management and the sustainable development of cities. Conventionally, multispectral or hyperspectral remote sensing images are applied to identify species. However, spectral profiles of trees on images are easily affected by surroundings and illuminations, resulting in urban trees of different species possibly having similar spectral features. The handheld laser scanning (HLS) technique can capture 3D structural information of trees and be confirmed to be effective in reducing the problem of spectral similarity through tree structural properties (TSP). TSP usually varies in different leaf conditions, especially TSP of tropical tree species. In this study, we investigated the effects of leaves on urban tropical tree species identification using HLS. A total of 89 metrics that characterized the TSP were evaluated, including 19 branches, 12 stems, 45 crowns, and 13 entire tree metrics. All metrics were derived under different leaf conditions. The correlation and importance of these metrics were further evaluated. Our results demonstrated that crown metrics perform the most important role in urban species identification in leaf-on and leaf-off conditions and that the combination of metrics derived in different leaf conditions can improve the identification accuracy. Furthermore, we discovered 9 robust metrics that perform well in all leaf conditions, including 3 crowns, 2 branches, 2 stems, and 2 entire tree metrics. These metrics give a deep understanding of numerous structural properties and provide a significant reference for the relevant structure-based classification of other tropical species. This study also illustrated that HLS could help to overcome the spectrum-related limitations and improve the efficiency of species identification and sustainable forest management.

Molecular Barrels as Potential Hosts: From Synthesis to Applications

Self-assembled discrete molecular architectures that show selective molecular recognition within their internal cavities are highly desirable. Such hosts often show guest recognition through several noncovalent interactions. This emulates the activity of naturally occurring enzymes and proteins. Research in the formation of 3D cages of different shapes and sizes has progressed rapidly since the development of coordination-driven self-assembly and dynamic covalent chemistry. Such molecular cages find applications in catalysis, stabilization of metastable molecules, purification of isomeric mixtures via selective encapsulation, and even in biomedical applications. Most of these applications stem from the ability of the host cages to bind guests strongly in a selective fashion, providing a suitable environment for the guests to perform their functions. Molecular cages having closed architectures with small windows either show poor encapsulation or inhibit easy guest release while those with wide open structures fail to form stable host-guest complexes. In this context, molecular barrels obtained by dynamic metal-ligand/covalent bond formation techniques possess optimized architectures. With a hollow-walled cavity and two large openings, molecular barrels satisfy the structural requirements for many applications. In this perspective, we will discuss in detail the synthetic strategies for obtaining barrels or barrel-like architectures employing dynamic coordination and covalent interactions, their structure-based classification, and their applications in catalysis, storing transient molecules, separation of chemicals, and photoinduced antibacterial activity. We aim to highlight the structural advantages of molecular barrels over other architectures for efficiently carrying out several functions and for the development of new applications.

PP.04 EGFR Structure-Based Classification and Presence of Commutations in Peruvian Patients With Advanced Lung Cancer

PP.04 EGFR Structure-Based Classification and Presence of Commutations in Peruvian Patients With Advanced Lung Cancer

The Usefulness of Nanotechnology in Improving the Prognosis of Lung Cancer

Lung cancer remains a major public health problem both in terms of incidence and specific mortality despite recent developments in terms of prevention, such as smoking reduction policies and clinical management advances. Better lung cancer prognosis could be achieved by early and accurate diagnosis and improved therapeutic interventions. Nanotechnology is a dynamic and fast-developing field; various medical applications have been developed and deployed, and more exist as proofs of concepts or experimental models. We aim to summarize current knowledge relevant to the use of nanotechnology in lung cancer management. Starting from the chemical structure-based classification of nanoparticles, we identify and review various practical implementations roughly organized as diagnostic or therapeutic in scope, ranging from innovative contrast agents to targeted drug carriers. Available data are presented starting with standards of practice and moving to highly experimental methods and proofs of concept; particularities, advantages, limits and future directions are explored, focusing on the potential impact on lung cancer clinical prognosis.

Phylogenetic tree reconstruction via graph cut presented using a quantum-inspired computer

Phylogenetic trees are essential tools in evolutionary biology that present information on evolutionary events among organisms and molecules. From a dataset of n sequences, a phylogenetic tree of (2n-5)!! possible topologies exists, and determining the optimum topology using brute force is infeasible. Recently, a recursive graph cut on a graph-represented-similarity matrix has proven accurate in reconstructing a phylogenetic tree containing distantly related sequences. However, identifying the optimum graph cut is challenging, and approximate solutions are currently utilized. Here, a phylogenetic tree was reconstructed with an improved graph cut using a quantum-inspired computer, the Fujitsu Digital Annealer (DA), and the algorithm was named the “Normalized-Minimum cut by Digital Annealer (NMcutDA) method”. First, a criterion for the graph cut, the normalized cut value, was compared with existing clustering methods. Based on the cut, we verified that the simulated phylogenetic tree could be reconstructed with the highest accuracy when sequences were diverged. Moreover, for some actual data from the structure-based protein classification database, only NMcutDA could cluster sequences into correct superfamilies. Conclusively, NMcutDA reconstructed better phylogenetic trees than those using other methods by optimizing the graph cut. We anticipate that when the diversity of sequences is sufficiently high, NMcutDA can be utilized with high efficiency.

EP16.03-032 Structure-Based Classification of Chinese Patients with Non-Canonical EGFR-Mutant Non-Small Cell Lung Cancer

Non-canonical EGFR mutations in non-small cell lung cancer (NSCLC) could be re-classified into four subtypes (Classical-like, P-loop and αC-helical compressing (PACC), T790M-like, and exon 20 loop insertions) based on unique structures as well as response to various types of tyrosine kinase inhibitors (TKIs). Since EGFR most frequently mutated in East-Asian population, it is clinically crucial to investigate the distribution of structure-based EGFR classification in Chinses NSCLC patients.

Structure-based classification of EGFR mutations in operable pre-invasive and invasive non-small cell lung cancer: a cross-sectional study.

BackgroundIt has been reported that the structure-based approach for defining functional groups of epidermal growth factor receptor (EGFR) mutations predicts the efficacy of EGFR inhibitors better than the traditional exon-based approach in the advanced stage. However, less is known about this structure-based classification of EGFR mutations in operable early-stage lung adenocarcinoma.MethodsNon-small cell lung cancer (NSCLC) patients with pathological stage I–III or adenocarcinoma in situ (AIS) who had EGFR mutations identified in next-generation sequencing (NGS) testing were recruited. Both exon-based and structure-based groupings of EGFR mutations were compared between the AIS and stage I–III patients using Fisher’s exact test.ResultsIn total 1,012 patients including 66 AIS and 946 stage I–III patients were analyzed in the study. A total of 1185 EGFR mutations were identified in the 1,012 NSCLC patients, of whom 84.39% harbored a single EGFR mutation and 15.61% harbored complex EGFR mutations. As expected, L858R was more common than 19del in our population (39.33% vs. 35.67%). Interestingly, concurrent L858R and 19del mutations were identified in 9 patients (0.89%), and all these patients were diagnosed with multiple primary lung cancer. A higher percentage of atypical EGFR mutations was identified in the AIS cohort than in the stage I–III NSCLC cohort (33.33% vs. 21.66%, P=0.03). According to the structure-based classification of EGFR mutations, 86.07%, 7.11%, 5.04%, and 1.78% of the EGFR mutations were classified as classical-like, P-loop and α C-helix compressing (PACC), exon 20 insertions (Ex20ins), and T790M-like mutations, respectively. The composition of EGFR mutations was different between patients <65 and ≥65 years (P=0.0267) but similar between patients with AIS and stage I–III NSCLC (P=0.1436). However, a higher percentage of Ex20ins occurred in younger (<65 years) patients, nonsmoking patients, and patients with AIS (6.7% vs. 2.5%, P=0.003; 5.8% vs. 0.8%, P=0.0107; and 10.6% vs. 4.7%, P=0.0423, respectively).ConclusionsThis large cross-sectional study delineated the structure-based classification of EGFR mutations in patients with operable NSCLC. While the traditional exon-based EGFR grouping showed difference between AIS and stage I–III NSCLC cohort, no difference was identified in the structural approach. Which approach had better prediction of targeted therapy efficacy in adjuvant settings warrants further investigation.

Structure-based assessment and druggability classification of protein–protein interaction sites

The featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.

Automatic identification of cassava leaf diseases utilizing morphological hidden patterns and multi-feature textures with a distributed structure-based classification approach

Automatic identification of cassava leaf diseases utilizing morphological hidden patterns and multi-feature textures with a distributed structure-based classification approach

Vertical Structure-Based Classification of Oceanic Eddy Using 3-D Convolutional Neural Network

The eddy identification is an important part of human cognition of the ocean. Significant achievements have been made by using sea level anomaly (SLA) data observed by the altimeter. However, the abundant eddies, which do not cause sea surface characteristic anomalies, cannot be identified. In this study, the eddy subsurface vertical structure-oriented 3-D neural network is developed to classify the oceanic eddies. This study is among the first that explores the ability of deep learning in eddy identification with vertical structure. First, the purified eddy profiles dataset is constructed based on the fact that the structure derived from vertical profiles is highly correlated with the sea surface topography detected by altimetry. Then, the eddy vertical structure-oriented 3-D neural network based on the residual network (ResNet) is constructed, which can classify the eddies as anticyclonic eddies (AEs), cyclonic eddies (CEs), and noneddies (NEs) effectively. Furthermore, the spatial and temporal features can be combined in the proposed network as external factors. Meanwhile, through 3-D convolutions and 3-D pooling, the proposed network is capable of modeling 3-D eddy data and can be extended to the deeper network structure. Finally, the classification experiments are implemented to validate the performance of the proposed methodology. The most striking result emerging from experiments is that the proposed method can expand the capacity of eddy identification by using vertical profiles as calibrated by altimetry with competitive classification performance. Together these results provide important insights into the application of artificial intelligence in oceanic eddy research.