Protein Structure Prediction Research Articles

Next Generation Sequencing in Detecting Oral Cancer Due to Tobacco Consumption

DNA sequence DNA Sequencing is the first step in establishing phylogenetic trees, protein structure prediction, diagnosis of cancer, discovery of drugs and hence its importance cannot be underestimated. DNA sequencing finds its use in the diagnosis of oral squamous cell carcinoma (OSCC). Oral Cancer is the most common occurring malignancies in the world, especially in India where the prevalence for smoking, Areca nut chewing coupled with a lifestyle that encourages these two activities as fashion are left many people diagnosed with OSCC. Patients with this OSCC are more likely unaware of its side effects and over time might suffer from facial deformity. The importance to understanding the symptoms, prevention and treatment of oral cancer is very much essential today. In this paper, we looked at over 2000 odd papers published and look at the correlation between the next Generation DNA sequencing algorithms (NGS) play an important role in diagnosis of OSCC. This is a further study on some of the papers which have highlighted NGS role in OSCC Diagnosis. We did like to see a comprehensive review on the papers published so far. In the discussion, we will see frequently mutated genes in the OSCC, recent discoveries and OSCC treatment based on the findings.

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Medical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively.

Exploring Aegilops caudata: A Comprehensive Study of the CslF6 Gene and β-Glucan.

In the quest for sustainable and nutritious food sources, exploration of ancient grains and wild relatives of cultivated cereals has gained attention. Aegilops caudata, a wild wheatgrass species, stands out as a promising genetic resource due to its potential for crop enhancement and intriguing nutritional properties. This manuscript investigates the CslF6 gene sequence and protein structure of Aegilops caudata, employing comparative analysis with other grass species to identify potential differences impacting β-glucan content. The study involves comprehensive isolation and characterization of the CslF6 gene in Ae. caudata, utilizing genomic sequence analysis, protein structure prediction, and comparative genomics. Comparisons with sequences from diverse monocots reveal evolutionary relationships, highlighting high identities with wheat genomes. Specific amino acid motifs in the CslF6 enzyme sequence, particularly those proximal to key catalytic motifs, exhibit variations among monocot species. These differences likely contribute to alterations in β-glucan composition, notably impacting the DP3:DP4 ratio, which is crucial for understanding and modulating the final β-glucan content. The study positions Ae. caudata uniquely within the evolutionary landscape of CslF6 among monocots, suggesting potential genetic divergence or unique functional adaptations within this species. Overall, this investigation enriches our understanding of β-glucan biosynthesis, shedding light on the role of specific amino acid residues in modulating enzymatic activity and polysaccharide composition.

Quantum chemical “Aufbau” principles: how to estimate the shape of highly flexible (bio-)polymers? A recursively extendable “chemion picture” of Euler-Hückel-type

An outline is given of how to split the n-dimensional space of torsion angles occurring in flexible (bio-)polymers (like alkanes, nucleic acids, or proteins, for instance) into n one-dimensional potential curves. Forthcoming applications will focus on the “protein folding problem,” beginning with polyglycine.ContextIn accordance with Euler’s topology rules, molecules are considered to be composed of “vertices” (atoms, ligands, bonding sites, functional groups, and bigger fragments). Following Hückel, each vertex is represented by only one basis function. Starting from the “monofocal” hydrids CH4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {CH}_{4}$$\\end{document}, NH3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {NH}_{3}$$\\end{document}, OH2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {OH}_{2}$$\\end{document}, FH, and SiH4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SiH}_{4}$$\\end{document}, PH3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {PH}_{3}$$\\end{document}, SH2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SH}_{2}$$\\end{document}, ClH as anchor units, “chemionic” Hamiltonians (of individual “chemion ensembles” and proportional nuclear charges) are constructed recursively, together with an appropriate basis set for the first five (normal) alkanes and some related oligomers like primary alcohols, alkyl amines, and alkyl chlorides.MethodsStandard methods (“Restricted Hartree-Fock RHF” and “Full Configuration Interaction FCI”) are used to solve the various stationary Schrödinger equations. Two software packages are indispensable: “SMILES” for integral evaluations over Slater-type orbitals (STO), and “Numerical Recipes” for matrix diagonalizations and inversions. While managing with only two-center repulsion integrals, “implicit multi-center integrations” lead us to the non-empirical fundament of Hoffmann’s “Extended-Hückel Theory.”

Genes encoding γ‑glutamyl‑transpeptidases in the allicin biosynthetic pathway in garlic (Alliumsativum).

Allicin is a thiosulphate molecule produced in garlic (Allium sativum) and has a wide range of biological actions and pharmaceutical applications. Its precursor molecule is the non-proteinogenic amino acid alliin (S-allylcysteine sulphoxide). The alliin biosynthetic pathway in garlic involves a group of enzymes, members of which are the γ-glutamyl-transpeptidase isoenzymes, Allium sativum γ-glutamyl-transpeptidase AsGGT1, AsGGT2 and AsGGT3, which catalyze the removal of the γ-glutamyl group from γ-glutamyl-S-allyl-L-cysteine to produce S-allyl-L-cysteine. This removal is followed by an S-oxygenation, which leads to the biosynthesis of alliin. The aim of the present study is to annotate previously discovered genes of garlic γ-glutamyl-transpeptidases, as well as a fourth candidate gene (AsGGT4) that has yet not been described. The annotation includes identifying the loci of the genes in the garlic genome, revealing the overall structure and conserved regions of these genes, and elucidating the evolutionary history of these enzymes through their phylogenetic analysis. The genomic structure of γ-glutamyl-transpeptidase genes is conserved; each gene consists of seven exons, and these genes are located on different chromosomes. AsGGT3 and AsGGT4 enzymes contain a signal peptide. To that end, the AsGGT3 protein sequence was corrected; four indel events occurring in AsGGT3 coding regions suggested that at least in the garlic variety Ershuizao, AsGGT3 may be a pseudogene. Finally, the use of protein structure prediction tools allowed the visualization of the tertiary structure of the candidate peptide.

Flexible protein-protein docking with a multitrack iterative transformer.

Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments, GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the Database of Interacting Protein Structures (DIPS) test set, GeoDock achieves a 43% top-1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody-antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold-Multimer. GeoDock attains an average inference speed of under 1 s on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).

Implicit model to capture electrostatic features of membrane environment.

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Glycoproteomic profile of human tissue-nonspecific alkaline phosphatase expressed in osteoblasts.

Tissue-nonspecific alkaline phosphatase (TNALP) is a glycoprotein expressed by osteoblasts that promotes bone mineralization. TNALP catalyzes the hydrolysis of the mineralization inhibitor inorganic pyrophosphate and ATP to provide inorganic phosphate, thus controlling the inorganic pyrophosphate/inorganic phosphate ratio to enable the growth of hydroxyapatite crystals. N-linked glycosylation of TNALP is essential for protein stability and enzymatic activity and is responsible for the presence of different bone isoforms of TNALP associated with functional and clinical differences. The site-specific glycosylation profiles of TNALP are, however, elusive. TNALP has 5 potential N-glycosylation sites located at the asparagine (N) residues 140, 230, 271, 303, and 430. The objective of this study was to reveal the presence and structure of site-specific glycosylation in TNALP expressed in osteoblasts. Calvarial osteoblasts derived from Alpl+/- expressing SV40 Large T antigen were transfected with soluble epitope-tagged human TNALP. Purified TNALP was analyzed with a lectin microarray, matrix-assisted laser desorption/ionization-time of flight mass spectrometry, and liquid chromatography with tandem mass spectrometry. The results showed that all sites (n = 5) were fully occupied predominantly with complex-type N-glycans. High abundance of galactosylated biantennary N-glycans with various degrees of sialylation was observed on all sites, as well as glycans with no terminal galactose and sialic acid. Furthermore, all sites had core fucosylation except site N271. Modelling of TNALP, with the protein structure prediction software ColabFold, showed possible steric hindrance by the adjacent side chain of W270, which could explain the absence of core fucosylation at N271. These novel findings provide evidence for N-linked glycosylation on all 5 sites of TNALP, as well as core fucosylation on 4 out of 5 sites. We anticipate that this new knowledge can aid in the development of functional and clinical assays specific for the TNALP bone isoforms.

A strained N-capping motif in α-helices of βαβ-units

A novel helical N-capping motif has been considered. It occurs in the βα-arches of right-handed βαβ-units and contains an N-cap residue in a sterically strained conformation. Moreover, this amino acid position contains almost no glycines, that could relieve strain. It was shown that the N-cap adopts this conformation as a result of the unusual convergence between the second and third amino acid positions of the α-helix (counting from the N-cap) and the second position of the preceding β-strand. This is achieved by the presence of glycines in the specified positions (i.e. positions i – 2, i + 2 and i + 3, if N-cap is i). The N-cap conformation is stabilized by a hydrogen bond between the backbone amide group in the second position of the α-helix and the carbonyl group in the first position of the β-strand. The occurrence of similar N-capping motifs in different types of βαβ-units was compared and their structural differences caused by the influence of the environment were described. Study results may be useful for protein design and ab initio prediction of the 3D protein structure.

Structural and Evolutionary Aspects of Plant Endocytosis.

Endocytosis is an essential eukaryotic process that maintains the homeostasis of the plasma membrane proteome by vesicle-mediated internalization. Its predominant mode of operation utilizes the polymerization of the scaffold protein clathrin forming a coat around the vesicle; therefore, it is termed clathrin-mediated endocytosis (CME). Throughout evolution, the machinery that mediates CME is marked by losses, multiplications, and innovations. CME employs a limited number of conserved structural domains and folds, whose assembly and connections are species dependent. In plants, many of the domains are grouped into an ancient multimeric complex, the TPLATE complex, which occupies a central position as an interaction hub for the endocytic machinery. In this review, we provide an overview of the current knowledge regarding the structural aspects of plant CME, and we draw comparisons to other model systems. To do so, we have taken advantage of recent developments with respect to artificial intelligence-based protein structure prediction.

Random, de novo, and conserved proteins: How structure and disorder predictors perform differently.

Understanding the emergence and structural characteristics of de novo and random proteins is crucial for unraveling protein evolution and designing novel enzymes. However, experimental determination of their structures remains challenging. Recent advancements in protein structure prediction, particularly with AlphaFold2 (AF2), have expanded our knowledge of protein structures, but their applicability to de novo and random proteins is unclear. In this study, we investigate the structural predictions and confidence scores of AF2 and protein language model-based predictor ESMFold for de novo and conserved proteins from Drosophila and a dataset of comparable random proteins. We find that the structural predictions for de novo and random proteins differ significantly from conserved proteins. Interestingly, a positive correlation between disorder and confidence scores (pLDDT) is observed for de novo and random proteins, in contrast to the negative correlation observed for conserved proteins. Furthermore, the performance of structure predictors for de novo and random proteins is hampered by the lack of sequence identity. We also observe fluctuating median predicted disorder among different sequence length quartiles for random proteins, suggesting an influence of sequence length on disorder predictions. In conclusion, while structure predictors provide initial insights into the structural composition of de novo and random proteins, their accuracy and applicability to such proteins remain limited. Experimental determination of their structures is necessary for a comprehensive understanding. The positive correlation between disorder and pLDDT could imply a potential for conditional folding and transient binding interactions of de novo and random proteins.

Genome-wide identification and integrated analysis of TCP genes controlling ginsenoside biosynthesis in Panax ginseng

Panax ginseng is an important medicinal plant, and ginsenosides are the main bioactive molecules of ginseng. The TCP (TBI, CYC, PCF) family is a group of transcription factors (TFs) that play an important role in plant growth and development, hormone signalling and synthesis of secondary metabolites. In our study, 78 PgTCP transcripts were identified from the established ginseng transcriptome database. A phylogenetic tree analysis showed that the 67 PgTCP transcripts with complete open reading frames were classified into three subfamilies, including CIN, PCF, and CYC/TB1. Protein structure analysis showed that PgTCP genes had bHLH structures. Chromosomal localization analysis showed that 63 PgTCP genes were localized on 17 of the 24 chromosomes of the Chinese ginseng genome. Expression pattern analysis showed that PgTCP genes differed among different lineages and were spatiotemporally specific. Coexpression network analysis indicated that PgTCP genes were coexpressed and involved in plant activities or metabolic regulation in ginseng. The expression levels of PgTCP genes from class I (PCF) were significantly downregulated, while the expression levels of PgTCP genes from class II (CIN and CYC/TB1) were upregulated, suggesting that TCP genes may be involved in the regulation of secondary metabolism in ginseng. As the PgTCP26-02 gene was found to be related to ginsenoside synthesis, its predicted protein structure and expression pattern were further analysed. Our results provide new insights into the origin, differentiation, evolution and function of the PgTCP gene family in ginseng, as well as the regulation of plant secondary metabolism.

Solving protein structures by combining structure prediction, molecular replacement and direct-methods-aided model completion.

Highly accurate protein structure prediction can generate accurate models of protein and protein-protein complexes in X-ray crystallography. However, the question of how to make more effective use of predicted models for completing structure analysis, and which strategies should be employed for the more challenging cases such as multi-helical structures, multimeric structures and extremely large structures, both in the model preparation and in the completion steps, remains open for discussion. In this paper, a new strategy is proposed based on the framework of direct methods and dual-space iteration, which can greatly simplify the pre-processing steps of predicted models both in normal and in challenging cases. Following this strategy, full-length models or the conservative structural domains could be used directly as the starting model, and the phase error and the model bias between the starting model and the real structure would be modified in the direct-methods-based dual-space iteration. Many challenging cases (from CASP14) have been tested for the general applicability of this constructive strategy, and almost complete models have been generated with reasonable statistics. The hybrid strategy therefore provides a meaningful scheme for X-ray structure determination using a predicted model as the starting point.

Molecular interactions of paraben family of pollutants with embryonic neuronal proteins of Danio rerio: A step ahead in computational toxicity towards adverse outcome pathway

The paraben family of endocrine disruptors exhibit persistent behaviours in aquatic matrices, having bio-accumulative effects and necessitating toxicity analysis and safe use, as well as prevention of food web penetration. In this study, the toxicity effects of 9 different parabens (Methyl, Ethyl, Propyl, Butyl, Heptyl, Isopropyl, Isobutyl, benzyl parabens and p-hydroxybenzoic acid) were studied against 17 neuronal proteins (Neurog1, Ascl1a, DLA, Syn2a, Ntn1a, Pitx2, and SoxB1, Her/Hes, Zic family) expressed during the early embryonic developmental stage of Danio rerio. The neuronal genes were selected as a biomarker to study the inhibitory effects on the cascade of genes expressed in the early developmental stage. The study uses trRossetta software to predict protein structures of neuronal genes, followed by structural refinement, energy minimisation, and active site prediction, evaluated using energy value, RC plot and ERRAT scores of PROCHECK and ERRAT programs. Compared to raw structures, highly confident predicted structures and quality scores were observed for refined protein with few exceptions. Based on the polarity and charge of the aminoacids, the probable pockets were identified using active site prediction, which were then used for molecular docking analysis. Further, the ADMET analysis, ligand likeliness and toxicological test revealed the paraben family of compounds as one of the most susceptible toxic and mutagenic compounds. The molecular docking results showed an interesting pattern of increasing binding affinity with increase in the carbon chains of paraben molecules. Benzyl Paraben showed higher binding affinities across all 17 neuronal proteins. Finally, gene co-occurrence/co-expression and protein-protein interaction studies using the STRING database depict that all proteins are functionally related and play essential roles in standard biological processes or pathways, conserved and expressed in diverse organisms. The interaction between paraben compounds and neuronal genes indicates high risks of inhibiting reactions in embryonic stages, emphasising the need for effective treatment measures and strict regulations.

Functional prediction of proteins from the human gut archaeome.

The human gastrointestinal tract contains diverse microbial communities, including archaea. Among them, Methanobrevibacter smithii represents a highly active and clinically relevant methanogenic archaeon, being involved in gastrointestinal disorders, such as inflammatory bowel disease and obesity. Herein, we present an integrated approach using sequence and structure information to improve the annotation of M. smithii proteins using advanced protein structure prediction and annotation tools, such as AlphaFold2, trRosetta, ProFunc, and DeepFri. Of an initial set of 873 481 archaeal proteins, we found 707 754 proteins exclusively present in the human gut. Having analysed archaeal proteins together with 87 282 994 bacterial proteins, we identified unique archaeal proteins and archaeal-bacterial homologs. We then predicted and characterized functional domains and structures of 73 unique and homologous archaeal protein clusters linked the human gut and M. smithii. We refined annotations based on the predicted structures, extending existing sequence similarity-based annotations. We identified gut-specific archaeal proteins that may be involved in defense mechanisms, virulence, adhesion, and the degradation of toxic substances. Interestingly, we identified potential glycosyltransferases that could be associated with N-linked and O-glycosylation. Additionally, we found preliminary evidence for interdomain horizontal gene transfer between Clostridia species and M. smithii, which includes sporulation Stage V proteins AE and AD. Our study broadens the understanding of archaeal biology, particularly M. smithii, and highlights the importance of considering both sequence and structure for the prediction of protein function.

Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development

The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved.

MehNet: a vigesimal-based model by amino acid melting points generates unique ID numbers for protein sequences

The amino acid encoding plays a pivotal role in machine learning-based methods for predicting protein structure and function, as well as in protein mapping techniques. Additionally, the classification of protein sequences presents its own challenges. The current study aims to assign a constant value to each amino acid, thereby creating distinctions among protein sequences. The datasets used in this study were obtained from the UniProt Knowledgebase. Subsequently, these datasets underwent preprocessing steps, and identical sequences were categorized under the same headings. Each amino acid was ranked based on its respective melting point and was assigned a vigesimal digit. These generated vigesimal digits were subsequently converted to decimal values. The centerpiece of this methodology was the me lting point h ashi n g t able, which was given the name ‘MehNet’. Ultimately, each protein sequence was assigned a unique identification number. This approach successfully digitized protein sequences. Notably, experiments involving randomly distributed vigesimal digits for amino acids did not yield results as promising as those achieved with MehNet. The model’s classification phase, which utilizes a k-nearest neighbors (kNN) classifier, demonstrates exceptional performance in miscellaneous viral sequences. It achieves high accuracy rates, with an overall accuracy of 99.75%. Notably, it achieves an outstanding accuracy of 99.92% for the Influenza C class, highlighting its ability to distinguish closely related viral sequences. Communicated by Ramaswamy H. Sarma

Prediction of protein structure and AI.

AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-function relationships from protein 3D structure models and can serve as a template or a reference for experimental structural analysis including X-ray crystallography, NMR and cryo-EM analysis. Its use is expanding among researchers, not only in structural biology but also in other research fields. Researchers are currently exploring the full potential of AlphaFold-generated protein models. Predicting disease severity caused by missense mutations is one such application. This article provides an overview of the 3D structural modeling of AlphaFold based on deep learning techniques and highlights the challenges in predicting the pathogenicity of missense mutations.