Prediction Of Interaction Sites Research Articles

Modeling Chromatography Binding through Molecular Dynamics Simulations with Resin Fragments.

Accurate atomistic modeling of the interactions of a chromatography resin with a solute can inform the selection of purification conditions for a product, an important problem in the biotech and pharmaceutical industries. We present a molecular dynamics simulation-based approach for the qualitative prediction of interaction sites (specificity) and retention times (affinity) of a protein for a given chromatography resin. We mimicked the resin with an unrestrained ligand composed of the resin headgroup coupled with successively larger fragments of the agarose backbone. The interactions of the ligand with the protein are simulated in an explicit solvent using the Replica Exchange Molecular Dynamics enhanced sampling approach in conjunction with Hydrogen Mass Repartitioning (REMD-HMR). We computed the ligand interaction surface from the simulation trajectories and correlated the features of the interaction surface with experimentally determined retention times. The simulation and analysis protocol were first applied to a series of ubiquitin mutants for which retention times on Capto MMC resin are available. The ubiquitin simulations helped identify the optimal ligand that was used in subsequent simulations on six proteins for which Capto MMC elution times are available. For each of the six proteins, we computed the interaction surface and characterized it in terms of a range of simulation-averaged residue-level physicochemical descriptors. Modeling of the salt concentrations required for elution with respect to the descriptors resulted in a linear fit in terms of aromaphilicity and Kyte-Doolittle hydrophobicity that was robust to outliers, showed high correlation, and correctly ranked the protein elution order. The physics-based model building approach described here does not require a large experimental data set and can be readily applied to different resins and diverse biomolecules.

Synergistic integration of deep learning with protein docking in cardiovascular disease treatment strategies.

This research delves into the exploration of the potential of tocopherol-based nanoemulsion as a therapeutic agent for cardiovascular diseases (CVD) through an in-depth molecular docking analysis. The study focuses on elucidating the molecular interactions between tocopherol and seven key proteins (1O8a, 4YAY, 4DLI, 1HW9, 2YCW, 1BO9 and 1CX2) that play pivotal roles in CVD development. Through rigorous in silico docking investigations, assessment was conducted on the binding affinities, inhibitory potentials and interaction patterns of tocopherol with these target proteins. The findings revealed significant interactions, particularly with 4YAY, displaying a robust binding energy of -6.39 kcal/mol and a promising Ki value of 20.84 μM. Notable interactions were also observed with 1HW9, 4DLI, 2YCW and 1CX2, further indicating tocopherol's potential therapeutic relevance. In contrast, no interaction was observed with 1BO9. Furthermore, an examination of the common residues of 4YAY bound to tocopherol was carried out, highlighting key intermolecular hydrophobic bonds that contribute to the interaction's stability. Tocopherol complies with pharmacokinetics (Lipinski's and Veber's) rules for oral bioavailability and proves safety non-toxic and non-carcinogenic. Thus, deep learning-based protein language models ESM1-b and ProtT5 were leveraged for input encodings to predict interaction sites between the 4YAY protein and tocopherol. Hence, highly accurate predictions of these critical protein-ligand interactions were achieved. This study not only advances theunderstanding of these interactions but also highlights deep learning's immense potential in molecular biology and drug discovery. It underscores tocopherol's promise as a cardiovascular disease management candidate, shedding light on its molecular interactions and compatibility with biomolecule-like characteristics.

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively. Additionally, it attains a Precision of 0.564 in PepBCL Set 1 and 0.527 in PepBCL Set 2, surpassing the performance of previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells, and other fields. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development.

Abstract 753: CEACAM1 induces microbiome and metabolite driven diversion of TGF-β signaling to promote colon cancer

Abstract Background: Colon cancer is rising in younger individuals in the United States. Potential causal factors are altered diets and gut microbiomes. Yet key signaling pathways, host-microbe interactions, and metabolites such as ammonia in cancer remain unclear. CEACAM1 responds to pathogenic microbes, interacts with the TGF-β/SPTBN1 and levels are raised in invasive colon cancer. Alterations in the TGF-β pathway occur ~40% of human GI cancers, and mouse models with disrupted TGF-β signaling (Tgfbr2−/−, Smad4+/−Sptbn1+/−, and Tgfb1−/−) develop colorectal cancer (CRC) that is dependent upon an altered gut microbiome. Our previous studies indicate the lipid metabolites (reactive aldehydes) form toxic adducts with cleaved SPTBN1 and divert TGF-β signaling to pro-oncogenic process. Poor prognosis CRC is associated with raised levels of microbial metabolites, such as ammonia. Thus, we hypothesized that “the intricate relationships among pathogen-binding CEACAM1, the gut microbiome, metabolites such as ammonia, and βII-Spectrin drive colon cancer development”. Methods: We examined gut microbiota composition, immune cell population, inflammation, and cancers in Smad4+/−Sptbn1+/− and Sptbn1LSKO mice fed a normal chow diet or a high-fat diet (HFD). We evaluated CEACAM1 and TGF-β member alterations in in CRCs using TCGA datasets. Alphafold-based molecular modeling predicted interaction sites between βII-Spectrin and CEACAM1 was followed by functional studies. Results: Increased CEACAM1 levels with alterations in TGF-β members were associated with lower survival (TCGA). Mice with disrupted TGF-β signaling (Smad4+/−Sptbn1+/−) develop CRC, have high CEACAM1 levels, and an altered gut microbiome signature. CEACAM1 interacts with βII-Spectrin, and levels are restored to normal in βII-Spectrin LSKO which restores the gut microbiome and blocks inflammation and cancer. Ammonia induces CASPASE-3 mediated cleavage of βII-Spectrin, forms toxic adducts and divert TGF-β signaling to pathological pro-oncogenic signaling, triggering CRC. Conclusion: We have found a critical role for CEACAMs in CRC cancer development, through diverting the TGF-β signaling pathway to a pro-oncogenic phenotype. CEACAM1 and βII-Spectrin are attractive therapeutic targets for CRC. Citation Format: Krishanu Bhowmick, Kazufumi Ohshiro, Xiyan Xiang, Sahara John, Xiaochun Yang, Kevin Idicula, Nicole Beauchemin, Raja Mazumder, Keith A. Crandall, Mohammad I. Hassan, Taj Mohammad, Lopa Mishra. CEACAM1 induces microbiome and metabolite driven diversion of TGF-β signaling to promote colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 753.

Novel manganese and nitrogen co-doped biochar based on sodium bicarbonate activation for efficient removal of bisphenol A: Mechanism insight and role analysis of manganese and nitrogen by combination of characterizations, experiments and density functional theory calculations

A novel porous manganese and nitrogen co-doped biochar (Mn-N@SBC) was synthesized via one-step pyrolysis, utilizing loofah agricultural waste as the precursor and NaHCO3 as the activator. The behavior of bisphenol A adsorbed on Mn-N@SBC was evaluated using static batch adsorption experiments. Compared to direct manganese-nitrogen co-doping, co-doping based on NaHCO3 activation significantly increased the specific surface area (231 to 1027 m2·g−1) and adsorption capacity (15 to 351 mg·g−1). Wide pH (2–10) and good resistance to cation/anion, humic acid and actual water demonstrated the robust adaptability of Mn-N@SBC to environmental factors. The significantly reduced specific surface area after adsorption, adverse effects of ethanol and phenanthrene on the removal of bisphenol A, and theoretically predicted interaction sites indicated the primary adsorption mechanisms involved pore filling, hydrophobicity, and π-π-electron-donor–acceptor interaction. This work presented an approach to create high-efficiency adsorbents from agricultural waste, offering theoretical and practical guidance for the removal of pollutants.

MEG-PPIS: a fast protein-protein interaction site prediction method based on multi-scale graph information and equivariant graph neural network.

Protein-protein interaction sites (PPIS) are crucial for deciphering protein action mechanisms and related medical research, which is the key issue in protein action research. Recent studies have shown that graph neural networks have achieved outstanding performance in predicting PPIS. However, these studies often neglect the modeling of information at different scales in the graph and the symmetry of protein molecules within three-dimensional space. In response to this gap, this article proposes the MEG-PPIS approach, a PPIS prediction method based on multi-scale graph information and E(n) equivariant graph neural network (EGNN). There are two channels in MEG-PPIS: the original graph and the subgraph obtained by graph pooling. The model can iteratively update the features of the original graph and subgraph through the weight-sharing EGNN. Subsequently, the max-pooling operation aggregates the updated features of the original graph and subgraph. Ultimately, the model feeds node features into the prediction layer to obtain prediction results. Comparative assessments against other methods on benchmark datasets reveal that MEG-PPIS achieves optimal performance across all evaluation metrics and gets the fastest runtime. Furthermore, specific case studies demonstrate that our method can predict more true positive and true negative sites than the current best method, proving that our model achieves better performance in the PPIS prediction task. The data and code are available at https://github.com/dhz234/MEG-PPIS.git.

Seq-InSite: sequence supersedes structure for protein interaction site prediction.

Proteins accomplish cellular functions by interacting with each other, which makes the prediction of interaction sites a fundamental problem. As experimental methods are expensive and time consuming, computational prediction of the interaction sites has been studied extensively. Structure-based programs are the most accurate, while the sequence-based ones are much more widely applicable, as the sequences available outnumber the structures by two orders of magnitude. Ideally, we would like a tool that has the quality of the former and the applicability of the latter. We provide here the first solution that achieves these two goals. Our new sequence-based program, Seq-InSite, greatly surpasses the performance of sequence-based models, matching the quality of state-of-the-art structure-based predictors, thus effectively superseding the need for models requiring structure. The predictive power of Seq-InSite is illustrated using an analysis of evolutionary conservation for four protein sequences. Seq-InSite is freely available as a web server at http://seq-insite.csd.uwo.ca/ and as free source code, including trained models and all datasets used for training and testing, at https://github.com/lucian-ilie/Seq-InSite.

SSCRB: Predicting circRNA-RBP Interaction Sites Using a Sequence and Structural Feature-Based Attention Model.

The prediction of interaction sites between circular RNA (circRNA) and RNA binding proteins (RBPs) is crucial for regulating diseases and discovering new treatment approaches. Computational models have been widely used to predict circRNA-RBP interaction sites due to the availability of genome-wide circRNA binding event data. However, efficiently obtaining multi-scale circRNA features to improve prediction accuracy remains a challenging problem. In this study, we propose SSCRB, a lightweight model for predicting circRNA-RBP interaction sites. Our model extracts both sequence and structural features of circRNA and incorporates multi-scale features through the attention mechanism. Furthermore, we develop an ensemble model by combining multiple submodels to enhance predictive performance and generalizability. We evaluate SSCRB on 37 circRNA datasets and compare it with other state-of-the-art methods. The average AUC of SSCRB is 97.66%, demonstrating its efficiency and robustness. SSCRB outperforms other methods in terms of prediction accuracy while requiring significantly fewer computational resources.

Protein–protein interaction site prediction by model ensembling with hybrid feature and self-attention

BackgroundProtein–protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences. Many feature extraction methods rely on the sliding window technique, which simply merges all the features of residues into a vector. The importance of some key residues may be weakened in the feature vector, leading to poor performance.ResultsWe propose a novel sequence-based method for PPI sites prediction. The new network model, PPINet, contains multiple feature processing paths. For a residue, the PPINet extracts the features of the targeted residue and its context separately. These two types of features are processed by two paths in the network and combined to form a protein representation, where the two types of features are of relatively equal importance. The model ensembling technique is applied to make use of more features. The base models are trained with different features and then ensembled via stacking. In addition, a data balancing strategy is presented, by which our model can get significant improvement on highly unbalanced data.ConclusionThe proposed method is evaluated on a fused dataset constructed from Dset186, Dset_72, and PDBset_164, as well as the public Dset_448 dataset. Compared with current state-of-the-art methods, the performance of our method is better than the others. In the most important metrics, such as AUPRC and recall, it surpasses the second-best programmer on the latter dataset by 6.9% and 4.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model, especially, the hybrid feature. We share our code for reproducibility and future research at https://github.com/CandiceCong/StackingPPINet.

Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks.

Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of computational models for PPIs prediction have been developed because experimental methods are slow and expensive. Most models employ a sliding window approach in which local neighbors are concatenated to present a target residue. However, those neighbors are not distinguished by pairwise information between a neighbor and the target. In this study, we propose a novel PPIs prediction model AttCNNPPISP, which combines attention mechanism and convolutional neural networks (CNNs). The attention mechanism dynamically captures the pairwise correlation of each neighbor-target pair within a sliding window, and therefore makes a better understanding of the local environment of target residue. And then, CNNs take the local representation as input to make prediction. Experiments are employed on several public benchmark datasets. Compared with the state-of-the-art models, AttCNNPPISP improves the prediction performance. Also, the experimental results demonstrate that the attention mechanism is effective in terms of constructing comprehensive context information of target residue.

Spatom: a graph neural network for structure-based protein-protein interaction site prediction.

Accurate identification of protein-protein interaction (PPI) sites remains a computational challenge. We propose Spatom, a novel framework for PPI site prediction. This framework first defines a weighted digraph for a protein structure to precisely characterize the spatial contacts of residues, then performs a weighted digraph convolution to aggregate both spatial local and global information and finally adds an improved graph attention layer to drive the predicted sites to form more continuous region(s). Spatom was tested on a diverse set of challenging protein-protein complexes and demonstrated the best performance among all the compared methods. Furthermore, when tested on multiple popular proteins in a case study, Spatom clearly identifies the interaction interfaces and captures the majority of hotspots. Spatom is expected to contribute to the understanding of protein interactions and drug designs targeting protein binding.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks.

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides. Here, we propose a deep graph convolutional network (GCN)-based method called GraphPPepIS to predict the interaction sites of protein-peptide complexes using protein and peptide structural information. We also propose a companion method, SeqPPepIS, for assisting with the lack of structural information and the flexibility of peptides. SepPPepIS replaces the peptide structural features in GraphPPepIS by learning features from peptide sequences. We performed a comprehensive evaluation of the benchmark data sets, and the results show that our two methods outperform state-of-the-art methods on the accurate interaction sites of both protein and peptide sides. We show that our methods can help improve protein-peptide docking. For docking data sets, our methods maintain robust performance in identifying binding sites, thereby enhancing the prediction of peptide binding poses. Finally, we visualized the analysis of protein and peptide graph embedding to demonstrate the learning ability of graph convolution in predicting interaction sites, which was mainly obtained through the shared parameters of a protein graph and peptide graph.

Improving protein-protein interaction site prediction using deep residual neural network

Accurate identification of protein-protein interaction (PPI) sites is significantly important for understanding the mechanism of life and developing new drugs. However, it is expensive and time-consuming to identify PPI sites using wet-lab experiments. Developing computational methods is a new road to identify PPI sites, which can accelerate the procedure of PPI-related research. In this study, we propose a novel deep learning-based method (called D-PPIsite) to improve the accuracy of sequence-based PPI site prediction. In D-PPIsite, four discriminative sequence-driven features, i.e., position specific scoring matrix, relative solvent accessibility, position information and physical properties, are employed to feed into a well-designed deep learning module, consisting of convolutional, squeeze and excitation, and fully connected layers, to learn a prediction model. To reduce the risk of a single prediction model getting stuck in local optima, multiple prediction models with different initialization parameters are selected and integrated into one final model using the mean ensemble strategy. Experimental results on five independent testing data sets demonstrate that the proposed D-PPIsite can achieve an average accuracy of 80.2% and precision of 36.9%, covering 53.5% of all PPI sites while achieving the average Matthews correlation coefficient value (0.330) that is significantly higher than most of existing state-of-the-art prediction methods. We implement a new standalone-version predictor for predicting PPI sites, which is freely available at https://github.com/MingDongup/D-PPIsite for academic use.

Identification of a peptide ligand for human ALDH3A1 through peptide phage display: Prediction and characterization of protein interaction sites and inhibition of ALDH3A1 enzymatic activity.

Aldehyde dehydrogenase 3A1 (ALDH3A1) by oxidizing medium chain aldehydes to their corresponding carboxylic acids, is involved in the detoxification of toxic byproducts and is considered to play an important role in antioxidant cellular defense. ALDH3A1 has been implicated in various other functions such as cell proliferation, cell cycle regulation, and DNA damage response. Recently, it has been identified as a putative biomarker of prostate, gastric, and lung cancer stem cell phenotype. Although ALDH3A1 has multifaceted functions in both normal and cancer homeostasis, its modes of action are currently unknown. To this end, we utilized a random 12-mer peptide phage display library to identify efficiently human ALDH3A1-interacting peptides. One prevailing peptide (P1) was systematically demonstrated to interact with the protein of interest, which was further validated in vitro by peptide ELISA. Bioinformatic analysis indicated two putative P1 binding sites on the protein surface implying biomedical potential and potent inhibitory activity of the P1 peptide on hALDH3A1 activity was demonstrated by enzymatic studies. Furthermore, in search of potential hALDH3A1 interacting players, a BLASTp search demonstrated that no protein in the database includes the full-length amino acid sequence of P1, but identified a list of proteins containing parts of the P1 sequence, which may prove potential hALDH3A1 interacting partners. Among them, Protein Kinase C Binding Protein 1 and General Transcription Factor II-I are candidates of high interest due to their cellular localization and function. To conclude, this study identifies a novel peptide with potential biomedical applications and further suggests a list of protein candidates be explored as possible hALDH3A1-interacting partners in future studies.

Improved compound–protein interaction site and binding affinity prediction using self-supervised protein embeddings

BackgroundCompound–protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound–protein interaction. For protein inputs, how to make use of protein primary sequence and tertiary structure information has impact on prediction results.ResultsIn this study, we propose a deep learning model based on a multi-objective neural network, which involves a multi-objective neural network for compound–protein interaction site and binding affinity prediction. We used several kinds of self-supervised protein embeddings to enrich our protein inputs and used convolutional neural networks to extract features from them. Our results demonstrate that our model had improvements in terms of interaction site prediction and affinity prediction compared to previous models. In a case study, our model could better predict binding sites, which also showed its effectiveness.ConclusionThese results suggest that our model could be a helpful tool for compound–protein related predictions.

HN-PPISP: a hybrid network based on MLP-Mixer for protein-protein interaction site prediction.

Biological experimental approaches to protein-protein interaction (PPI) site prediction are critical for understanding the mechanisms of biochemical processes but are time-consuming and laborious. With the development of Deep Learning (DL) techniques, the most popular Convolutional Neural Networks (CNN)-based methods have been proposed to address these problems. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in protein sequences. Current methods cannot efficiently explore the nature of Position Specific Scoring Matrix (PSSM), secondary structure and raw protein sequences by processing them all together. For PPI site prediction, how to effectively model the PPI context with attention to prediction remains an open problem. In addition, the long-distance dependencies of PPI features are important, which is very challenging for many CNN-based methods because the innate ability of CNN is difficult to outperform auto-regressive models like Transformers. To effectively mine the properties of PPI features, a novel hybrid neural network named HN-PPISP is proposed, which integrates a Multi-layer Perceptron Mixer (MLP-Mixer) module for local feature extraction and a two-stage multi-branch module for global feature capture. The model merits Transformer, TextCNN and Bi-LSTM as a powerful alternative for PPI site prediction. On the one hand, this is the first application of an advanced Transformer (i.e. MLP-Mixer) with a hybrid network for sequence-based PPI prediction. On the other hand, unlike existing methods that treat global features altogether, the proposed two-stage multi-branch hybrid module firstly assigns different attention scores to the input features and then encodes the feature through different branch modules. In the first stage, different improved attention modules are hybridized to extract features from the raw protein sequences, secondary structure and PSSM, respectively. In the second stage, a multi-branch network is designed to aggregate information from both branches in parallel. The two branches encode the features and extract dependencies through several operations such as TextCNN, Bi-LSTM and different activation functions. Experimental results on real-world public datasets show that our model consistently achieves state-of-the-art performance over seven remarkable baselines. The source code of HN-PPISP model is available at https://github.com/ylxu05/HN-PPISP.

RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction.

The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational methods have been proposed in the past. However, the majority of the past methods are time consuming and lack accuracy. Hence, coming up with an effective computational method is necessary. In this article, we present a novel computational model called RGN (residue-based graph attention and convolutional network) to predict PPI sites. In our paper, the protein is treated as a graph. The amino acid can be seen as the node in the graph structure. The position-specific scoring matrix, hidden Markov model, hydrogen bond estimation algorithm, and ProtBert are applied as node features. The edges are decided by the spatial distance between the amino acids. Then, we utilize a residue-based graph convolutional network and graph attention network to further extract the deeper feature. Finally, the processed node feature is fed into the prediction layer. We show the superiority of our model by comparing it with the other four protein structure-based methods and five protein sequence-based methods. Our model obtains the best performance on all the evaluation metrics (accuracy, precision, recall, F1 score, Matthews correlation coefficient, area under the receiver operating characteristic curve, and area under the precision recall curve). We also conduct a case study to demonstrate that extracting the protein information from the protein structure perspective is effective and points out the difficult aspect of PPI site prediction.

SENSDeep: An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction.

The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein-protein interaction sites (PPISs), these are costly, time-consuming, and require expertise. Therefore, many computational methods have been proposed to accelerate this type of research, but they are generally insufficient to predict PPISs accurately. There is a need for development in this field. In this study, we introduce a new PPISs prediction method. This method is a sequence-based Stacking ENSemble Deep (SENSDeep) learning method that has an ensemble learning model including the models of RNN, CNN, GRU sequence to sequence (GRUs2s), GRU sequence to sequence with an attention layer (GRUs2satt) and a multilayer perceptron. Two embedded features, secondary structure, and protein sequence information are added to the training data set in addition to twelve existing features to improve the prediction performance of the method. SENSDeep trained on the training data set without two extra features obtains a better performance on some of the independent testing data sets than that of the other methods in the literature, especially on scoring metrics of sensitivity, F1, MCC, and AUPRC, having increments up to 63.5%, 19.3%, 18.5%, 11.4%, respectively. It is shown that the added extra features improve the performance of the method by having almost the same performance with less data as the method trained on the data set without these added features. On the other hand, different sizes of the sliding window are tried on the data sets and an optimal sliding window size for SENSDeep is found. Moreover, SENSDeep has also been compared to structure-based methods. Some of these methods have been found to perform better. Using SENSDeep obtained by training with both training data sets, PPISs prediction examples of various proteins that are not in these training data sets are also presented. Furthermore, execution times for SENSDeep and its submodels are shown. https://github.com/enginaybey/SENSDeep.

PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention.

Cellular functions are governed by proteins, and, while some proteins work independently, most work by interacting with other proteins. As a result it is crucially important to know the interaction sites that facilitate the interactions between the proteins. Since the experimental methods are costly and time consuming, it is essential to develop effective computational methods. We present PITHIA, a sequence-based deep learning model for protein interaction site prediction that exploits the combination of multiple sequence alignments and learning attention. We demonstrate that our new model clearly outperforms the state-of-the-art models on a wide range of metrics. In order to provide meaningful comparison, we update existing test datasets with new information regarding interaction site, as well as introduce an additional new testing dataset which resolves the shortcomings of the existing ones.

Effect of ultrasonic cavitation on the formation of soy protein isolate – rice starch complexes, and the characterization and prediction of interaction sites using molecular techniques

Protein-carbohydrate interactions occur naturally in glycoproteins which are highly stable in nature and are involved in various food complexes and can enhance the quality and functional properties of foods. In the current study, we characterized the protein-carbohydrate complex formed between commercial soy protein isolate and rice starch using different treatments namely heat treatment alone, ultrasound treatment alone, combination of ultrasound and heat treatment and mixing alone. The structural data obtained using circular dichroism indicated that during the complex formation, the α-helix values were reduced by a maximum of 67% compared to soy protein isolate alone. The crystalline nature of the complexes formed by ultrasound treatment preserved the techno-functional properties as compared to complexes formed by heat treatments. The FTIR analysis of the complexes formed indicated the formation of glycosidic bond. Molecular docking analysis revealed the interaction between the complexes occurred due to hydrogen bonds which make the proteins more stable in nature thus enhancing their denaturation temperature. Glutamine, Proline and Arginine present in the D subunit of 7S 3AUP interacts with the starch molecule. The obtained results suggest that sonication combined with heat treatment led to higher interaction between the soy proteins isolate and rice starch.