Protein Primary Sequence Research Articles

Development of Tertiary Structure of Laccase from Geobacillus uzenensis using Homology Modelling

Laccases are a versatile enzyme with immense potential in various biotechnological applications. In this study, the protein structure prediction of laccase from Geobacillus uzenensis, a bacteria known for its robust enzymatic activity is focused. The protein structural elucidation of this laccase is crucial to understand its substrate binding and overall functionality. Protein structure can be determined by computational prediction, which is commonly known as homology modelling. The three-dimensional (3D) model was built based on the available crystal structures of related laccase enzymes and aligned with the primary sequence of the target protein. The resulting model exhibited a high degree of structural similarity and conservation of key catalytic residues, supporting its reliability for further analyses. Firstly, the suitable template was identified with 85.61% of sequence identity determined by sequence alignment. Then, MODELLER program was used to predict the model using the method of satisfaction of spatial restraints. The model was then analyzed for its quality by computational analysis tools such as Ramachandran's Plot. Finally, the binding site of the protein was identified using the GRaSP computational tool. These findings provide significant insight on comprehensive analysis of the laccase from G. uzenensis, by providing a structural framework to unravel its functional properties and substrate specificity.

Physiology, Pathophysiology and Clinical Relevance of D-Amino Acids Dynamics: From Neurochemistry to Pharmacotherapy.

Over three decades ago, two independent groups of investigators identified free D-aspartic and later D-serine in specific brain nuclei and endocrine glands. This finding revealed a novel, non-proteinogenic role of these molecules. Moreover, the finding that aged proteins from the human eye crystallin, teeth, bone, blood vessels or the brain incorporate D-aspartic acids to specific primary protein sequences fostered the hypothesis that aging might be related to D-amino acid isomerization of body proteins. The experimental confirmation that schizophrenia and neurodegenerative diseases modify plasma free D-amino acids or tissue levelsnurtured the opportunity of using D-amino acids as therapeutic agents for several disease treatments, a strategy that prompted the successful current application of D-amino acids to human medicine.

Prediction of Anti‐Freezing Proteins From Their Evolutionary Profile

ABSTRACTPrediction of antifreeze proteins (AFPs) holds significant importance due to their diverse applications in healthcare. An inherent limitation of current AFP prediction methods is their reliance on unreviewed proteins for evaluation. This study evaluates, proposed and existing methods on an independent dataset containing 80 AFPs and 73 non‐AFPs obtained from Uniport, which have been already reviewed by experts. Initially, we constructed machine learning models for AFP prediction using selected composition‐based protein features and achieved a peak AUROC of 0.90 with an MCC of 0.69 on the independent dataset. Subsequently, we observed a notable enhancement in model performance, with the AUROC increasing from 0.90 to 0.93 upon incorporating evolutionary information instead of relying solely on the primary sequence of proteins. Furthermore, we explored hybrid models integrating our machine learning approaches with BLAST‐based similarity and motif‐based methods. However, the performance of these hybrid models either matched or was inferior to that of our best machine‐learning model. Our best model based on evolutionary information outperforms all existing methods on independent/validation dataset. To facilitate users, a user‐friendly web server with a standalone package named “AFPropred” was developed (https://webs.iiitd.edu.in/raghava/afpropred).

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest

Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.

Exploring the sequence and structural determinants of the energy landscape from thermodynamically stable and kinetically trapped subtilisins: ISP1 and SbtE.

A protein's energy landscape, all the accessible conformations, their populations, and their dynamics of interconversion, is encoded in its primary sequence. While we have a good understanding of how a protein's primary sequence encodes its native state, we have a much weaker understanding of how sequence encodes the kinetic barriers such as unfolding and refolding. Here we have looked at two subtiliase homologs from the Bacillus subtilis, Intracellular Subtilisin Protease 1 (ISP1) and Subtilisin E (SbtE) that are expected to have very different dynamics. As an intracellular protein, ISP1 has a small pro-domain thought to act simply as a zymogen, whereas the extracellular SbtE has a large pro-domain required for folding. We examined the global and local energetics of the mature proteases and how each pro-domain impacts their landscapes. We find that ISP1's pro-domain has limited impact on the energy landscape while the mature SbtE is thermodynamically unstable and kinetically trapped. The impact of the pro-domain has opposite effects on the flexibility of the core of the protein. ISP1's core becomes more flexible while SbtE's core becomes more rigid. ISP1 contains a conserved amino-acid insertion not present in extracellular subtilisin proteases, which points to a potential source for these differences. These homologs are an extreme example of how changes in the primary sequence can dramatically alter a proteins energy landscape, both stability and dynamics, and highlight the need for large scale, high throughput studies on the relationship between primary sequence and conformational dynamics.

Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration

Background: Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction, many of these methods primarily concentrate on the amino acid sequence of proteins. Yet, the interactions between drug compounds and targets occur within distinct segments within the protein structures, whereas the primary sequence primarily captures global protein features. Consequently, it falls short of fully elucidating the intricate relationship between drugs and their respective targets. Objective: This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating information about the secondary structure of proteins. Methods: In our research, both the primary sequence of protein and the secondary structure of protein were leveraged for protein representation. While the primary sequence played the role of the overarching feature, the secondary structure was employed as the localized feature. Convolutional neural networks and graph neural networks were utilized to independently model the intricate features of target proteins and drug compounds. This approach enhanced our ability to capture drugtarget interactions more effectively Results: We have introduced a novel method for predicting DTA. In comparison to DeepDTA, our approach demonstrates significant enhancements, achieving a 3.9% increase in the Concordance Index (CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA dataset. Conclusion: In conclusion, our results unequivocally demonstrate that augmenting DTA prediction with the inclusion of the protein's secondary structure as a localized feature yields significantly improved accuracy compared to relying solely on the primary structure.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

Targeting STING oligomerization with licochalcone D ameliorates STING-driven inflammatory diseases.

The development of STING inhibitors for the treatment of STING-related inflammatory diseases continues to encounter significant challenges. The activation of STING is a multi-step process that includes binding with cGAMP, self-oligomerization, and translocation from the endoplasmic reticulum to the Golgi apparatus, ultimately inducing the expression of IRF3 and NF-κB-mediated interferons and inflammatory cytokines. It has been demonstrated that disruption of any of these steps can effectively inhibit STING activation. Traditional structure-based drug screening methodologies generally focus on specific binding sites. In this study, a TransformerCPI model based on protein primary sequences and independent of binding sites is employed to identify compounds capable of binding to the STING protein. The natural product Licochalcone D (LicoD) is identified as a potent and selective STING inhibitor. LicoD does not bind to the classical ligand-binding pocket; instead, it covalently modifies the Cys148 residue of STING. This modification inhibits STING oligomerization, consequently suppressing the recruitment of TBK1 and the nuclear translocation of IRF3 and NF-κB. LicoD treatment ameliorates the inflammatory phenotype in Trex1-1- mice and inhibits the progression of DSS-induced colitis and AOM/DSS-induced colitis-associated colon cancer (CAC). In summary, this study reveals the potential of LicoD in treating STING-driven inflammatory diseases. It also demonstrates the utility of the TransformerCPI model in discovering allosteric compounds beyond the conventional binding pockets.

Enzyme Engineering by Force: DNA Springs for the Modulation of Biocatalytic Trajectories.

The engineering of enzymatic activity generally involves alteration of the protein primary sequences, which introduce structural changes that give rise to functional improvements. Mechanical forces have been used to interrogate protein biophysics, leading to deep mechanistic insights in single-molecule studies. Here, we use simple DNA springs to apply small pulling forces to perturb the active site of a thermostable alcohol dehydrogenase. Methods were developed to enable the study of different spring lengths and spring orientations under bulk catalysis conditions. Tension applied across the active site expanded the binding pocket volume and shifted the preference of the enzyme for longer chain-length substrates, which could be tuned by altering the spring length and the resultant applied force. The substrate specificity changes did not occur when the DNA spring was either severed or rotated by ∼90°. These findings demonstrate an alternative approach in protein engineering, where active site architectures can be dynamically and reversibly remodeled using applied mechanical forces.

G–PLIP: Knowledge graph neural network for structure-free protein–ligand bioactivity prediction

Protein–ligand interactions (PLIs) determine the efficacy and safety profiles of small molecule drugs. Existing methods rely on either structural information or resource-intensive computations to predict PLI, casting doubt on whether it is possible to perform structure-free PLI predictions at low computational cost. Here we show that a light-weight graph neural network (GNN), trained with quantitative PLIs of a small number of proteins and ligands, is able to predict the strength of unseen PLIs. The model has no direct access to structural information about the protein–ligand complexes. Instead, the predictive power is provided by encoding the entire chemical and proteomic space in a single heterogeneous graph, encapsulating primary protein sequence, gene expression, the protein–protein interaction network, and structural similarities between ligands. This novel approach performs competitively with, or better than, structure-aware models. Our results suggest that existing PLI prediction methods may be improved by incorporating representation learning techniques that embed biological and chemical knowledge.

Protein-Protein Interaction Prediction via Structure-Based Deep Learning.

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm.

Evolutionary origins of archaeal and eukaryotic RNA-guided RNA modification in bacterial IS110 transposons.

Transposase genes are ubiquitous in all domains of life and provide a rich reservoir for the evolution of novel protein functions. Here we report deep evolutionary links between bacterial IS110 transposases, which catalyze RNA-guided DNA recombination using bridge RNAs, and archaeal/eukaryotic Nop5-family proteins, which promote RNA-guided RNA 2'-O-methylation using C/D-box snoRNAs. Based on conservation in the protein primary sequence, domain architecture, and three-dimensional structure, as well as common architectural features of the non-coding RNA components, we propose that programmable RNA modification emerged via exaptation of components derived from IS110-like transposons. Alongside recent studies highlighting the origins of CRISPR-Cas9 and Cas12 in IS605-family transposons, these findings underscore how recurrent domestication events of transposable elements gave rise to complex RNA-guided biological mechanisms.

Deep learning of antibody epitopes using positional permutation vectors

BackgroundThe accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. ResultsHere we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate “induced fit” biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training. ConclusionsWith the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

A methodological exploration of distinguishing hair quality based on hair proteomics

Hair is an advantageous biological sample due to its recordable, collectable, and storable nature. Hair's primary components are keratin and keratin-associated proteins. Owing to its abundance of cystine, keratin possesses impressive mechanical strength and chemical stability, formed by creating disulfide bonds as crosslinks within the protein peptide chain. Furthermore, keratin is cross-linked with keratin-associated proteins to create a complex network structure that provides the hair with strength and rigidity. Protein extraction serves as the foundation for hair analysis research. Bleaching hair causes damage to the structure between keratin and keratin-associated proteins, resulting in texture issues and hair breakage. This article outlines various physical treatment methods and lysate analysis that enhance the efficiency of hair protein extraction. The PLEE method achieves a three-fold increase in hair protein extraction efficiency when using a lysis solution containing SDS and combining high temperatures with intense shaking, compared to previous methods found in literature. We utilized the PLEE method to extract hair from both normal and damaged groups. Normal samples identified 156–157 proteins, including 51 keratin and keratin-associated proteins. The damaged group consisted of 155–158 identified proteins, of which 48–50 were keratin and keratin-associated proteins. Bleaching did not cause any notable difference in the protein identification of hair. However, it did reduce coverage of keratin and keratin-associated proteins significantly. Our hair protein extraction method provides extensive coverage of the hair proteome. Our findings indicate that bleaching damage results in subpar hair quality due to reduced coverage of protein primary sequences in keratin and keratin-associated proteins.

Analysis and prediction of interactions between transmembrane and non-transmembrane proteins

BackgroundMost of the important biological mechanisms and functions of transmembrane proteins (TMPs) are realized through their interactions with non-transmembrane proteins(nonTMPs). The interactions between TMPs and nonTMPs in cells play vital roles in intracellular signaling, energy metabolism, investigating membrane-crossing mechanisms, correlations between disease and drugs.ResultsDespite the importance of TMP-nonTMP interactions, the study of them remains in the wet experimental stage, lacking specific and comprehensive studies in the field of bioinformatics. To fill this gap, we performed a comprehensive statistical analysis of known TMP-nonTMP interactions and constructed a deep learning-based predictor to identify potential interactions. The statistical analysis describes known TMP-nonTMP interactions from various perspectives, such as distributions of species and protein families, enrichment of GO and KEGG pathways, as well as hub proteins and subnetwork modules in the PPI network. The predictor implemented by an end-to-end deep learning model can identify potential interactions from protein primary sequence information. The experimental results over the independent validation demonstrated considerable prediction performance with an MCC of 0.541.ConclusionsTo our knowledge, we were the first to focus on TMP-nonTMP interactions. We comprehensively analyzed them using bioinformatics methods and predicted them via deep learning-based solely on their sequence. This research completes a key link in the protein network, benefits the understanding of protein functions, and helps in pathogenesis studies of diseases and associated drug development.

Comparative physicochemical and structural characterisation studies establish high biosimilarity between BGL-ASP and reference insulin aspart

Biosimilar insulin analogues are increasing market access for diabetic patients globally. Scientific establishment of biosimilarity is cornerstone of this key change in the medical landscape. BGL-ASP is a biosimilar insulin aspart developed by BioGenomics Limited, India. BioGenomics has considered a stepwise approach in generating the totality of evidence required to establish similarity with reference product. Insulin aspart is a recombinant rapid-acting human insulin analogue utilised in the treatment of type-1 and type-2 diabetes mellitus. The single amino acid substitution at position B28 where proline is replaced with aspartic acid results in a decreased propensity to form hexamers, thus increasing the absorption rate on subcutaneous administration compared to native insulin. In order to establish the safety and efficacy of BGL-ASP, the critical quality attributes (CQAs) of BGL-ASP are identified based on the impact created on biological activity, pharmacokinetic/pharmacodynamic (PK/PD), immunogenicity and safety. The CQAs of insulin aspart are related to product structure, purity and functionality and are characterised using a series of state-of-the-art orthogonal analytical tools. The primary protein sequence, the secondary, tertiary and quaternary structure are found to be highly similar for BGL-ASP and reference product. The product related impurities of insulin aspart and the assay content are determined using high performance liquid chromatography (HPLC) based analysis and is similar for BGL-ASP and reference insulin aspart sourced from United States of America (US), Europe Union (EU) and India. The safety, efficacy and immunogenicity of BGL-ASP is also found to be comparable with reference product and is confirmed through the clinical trials conducted as recommended by International Council for Harmonisation of Technical Requirements of Pharmaceuticals for Human Use (ICH) and European Medicines Agency (EMA) guidelines. The data encompassed in this study demonstrates that reference insulin aspart and BGL-ASP are highly similar in terms of structural, physicochemical, and biological properties, thus confirming its safety and efficacy for usage as potential alternative economical medicinal treatment for diabetes mellitus.

TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms.

Understanding protein thermostability is essential for numerous biotechnological applications, but traditional experimental methods are time-consuming, expensive, and error-prone. Recently, deep learning (DL) techniques from natural language processing (NLP) was extended to the field of biology, since the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar. In this study, we developed TemBERTure, a DL framework that predicts thermostability class and melting temperature from protein sequences. Our findings emphasize the importance of data diversity for training robust models, especially by including sequences from a wider range of organisms. Additionally, we suggest using attention scores from Deep Learning models to gain deeper insights into protein thermostability. Analyzing these scores in conjunction with the 3D protein structure can enhance understanding of the complex interactions among amino acid properties, their positioning, and the surrounding microenvironment. By addressing the limitations of current prediction methods and introducing new exploration avenues, this research paves the way for more accurate and informative protein thermostability predictions, ultimately accelerating advancements in protein engineering. TemBERTure model and the data are available at: https://github.com/ibmm-unibe-ch/TemBERTure.

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git.

A Library of Elastin-like Proteins with Tunable Matrix Ligands for In Vitro 3D Neural Cell Culture.

Hydrogels with encapsulated cells have widespread biomedical applications, both as tissue-mimetic 3D cultures in vitro and as tissue-engineered therapies in vivo. Within these hydrogels, the presentation of cell-instructive extracellular matrix (ECM)-derived ligands and matrix stiffness are critical factors known to influence numerous cell behaviors. While individual ECM biopolymers can be blended together to alter the presentation of cell-instructive ligands, this typically results in hydrogels with a range of mechanical properties. Synthetic systems that allow for the facile incorporation and modulation of multiple ligands without modification of matrix mechanics are highly desirable. In the present work, we leverage protein engineering to design a family of xeno-free hydrogels (i.e., devoid of animal-derived components) consisting of recombinant hyaluronan and recombinant elastin-like proteins (ELPs), cross-linked together with dynamic covalent bonds. The ELP components incorporate cell-instructive peptide ligands derived from ECM proteins, including fibronectin (RGD), laminin (IKVAV and YIGSR), collagen (DGEA), and tenascin-C (PLAEIDGIELTY and VFDNFVL). By carefully designing the protein primary sequence, we form 3D hydrogels with defined and tunable concentrations of cell-instructive ligands that have similar matrix mechanics. Utilizing this system, we demonstrate that neurite outgrowth from encapsulated embryonic dorsal root ganglion (DRG) cultures is significantly modified by cell-instructive ligand content. Thus, this library of protein-engineered hydrogels is a cell-compatible system to systematically study cell responses to matrix-derived ligands.

Characterization of VIM-71, a novel VIM-type metallo-β-lactamase variant encoded by an integrative and conjugative element recovered from a Vibrio alginolyticus strain in China

A novel VIM-type metallo-β-lactamase variant, VIM-71, which is encoded by a multidrug-resistant Vibrio alginolyticus strain recovered from a shrimp sample in China, was identified. Compared to VIM-1, VIM-71 differs in 22 amino acid positions based on the primary protein sequence and confers a similar resistance profile to penicillins, but the level of resistance to carbapenems encoded by this enzyme was lower than that of VIM-1. The blaVIM-71 gene was found located in an integrative and conjugative element of the SXT/R391 family in the chromosome. These findings implied that genetic elements that encode clinically important carbapenemases continue to evolve in Vibrio spp.