Recently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14  competition. However, the question of the mechanism behind the protein folding process itself remains unanswered. Correctly predicting the structure also does not solve the problem of, for example, amyloid proteins, where a polypeptide chain with an unaltered sequence adopts a different 3D structure.This work was an attempt at explaining the structural variation by considering the contribution of the environment to protein structuring. The application of the fuzzy oil drop (FOD) model to assess the validity of the selected models provided in the CASP13, CASP14 and CASP15 projects reveals the need for an environmental factor to determine the 3D structure of proteins. Consideration of the external force field in the form of polar water (Fuzzy Oil Drop) and a version modified by the presence of the hydrophobic compounds, FOD-M (FOD-Modified) reveals that the protein folding process is environmentally dependent. An analysis of selected models from the CASP competitions indicates the need for structure prediction as dependent on the consideration of the protein folding environment.The conditions governed by the environment direct the protein folding process occurring in a certain environment. Therefore, the variation of the external force field should be taken into account in the models used in protein structure prediction. The protein folding problem has a long history of identification of the phenomenon in which proteins adopt a 3D structure in an explicitly defined and reproducible way , 2. MoniThe AI method used provides a correct structure with a high accuracy score on the GDT_TS  scale used in CASP (it is the indicator used as a criterion for the degree of accuracy of model structure prediction against the target structure), which considers multiple similarity assessment criteria . The AI However, the question of ‘Why proteins fold the way they do?’ remains unanswered. In this work, the degree of accuracy of the predicted structure was interpreted based on the consideration of the protein folding environment. The final structure of a given protein varies with the environment. Hence, using a steady-state internal force field (including preferred inter-amino acid distances) for all proteins may result in the success for one protein and a failure for another protein. The question arises as to why a jointly applied computational methods have not made any significant progress (WeFold project) , 24.The Bhageerath-H server representing the hybrid ab initio/homology modelling reaching models of high accordance in CASP10 for water soluble monomeric proteins is freely available .The three-track neural network linking a one-dimensional sequence level with the distance map, and the coordinate level expressing the integration of these three approaches was applied in CASP14 with good results .Very popular recently techniques based on deep learning applied to the combination of template and multiple sequence alignment deliver some progress due to permanently significantly growing number of available structures making the comparable prediction more effective . The intThis approach is applied in the ProFitFun-Meta server, which is freely available , 32.The analysis of the reasons for the variation in the degree of accuracy of the predicted structures was based on this work on the assumption of environmental contribution in the protein structuring process. A diverse environment governs the protein folding process. The absence of a diverse environment in the protein folding model eliminates a broad spectrum of structural forms. The possibility of considering conditions imposed by the environment is offered by the fuzzy oil drop (FOD) model, as well as its modified version (FOD-M) , 34. TheThe ab initio technique poses the challenge of finding the mechanism of the protein folding process that leads to the prediction of the correct structure by understanding such mechanism. This work demonstrates that this is not possible if the notation of the external force field of the environment origin is not considered. Environmental differentiation – as shown by previous analyses – has a significant impact on protein structuring , 36. An The selection of proteins analysed is limited by the condition of the availability of the protein structures acting as targets in the CASP project and the models proposed by the CASP project participants. The subjective choice was also driven by the extremes: the best and the worst results.The target structures CASP13): T0953s2-D3 (PDB ID-6F45 ), T0990-: T0953s2Proteins with an all helical structure are considered to be easy. However several other factors contribute to the computational structural model ability expressed by structural difficulty (SD) taking into account secondary structures, homology and physicochemical features of protein . Also thProteins with the code PDB ID—6POO   demonstA comparative analysis was carried out for the following:https://predictioncenter.org/ .The database from which the results for this analysis were taken is available at The external force field generated by the aqueous environment becomes apparent in the micellization process of bi-polar molecules, which, avoiding the entropically unfavourable contact of hydrophobic parts with polar water, form structures with an exposed polar surface, isolating hydrophobic fragments concentrated in the central part of the micelle. The description of such a hydrophobicity distribution is expressed by a 3D Gaussian function that spans the protein body:X, σY and σZ, it is possible to describe globular forms of arbitrary size and shape. ‘Hi’ expresses the idealised hydrophobicity value assuming a micelle-like system. This value assigned to the position of the effective atom (the averaged position of the atoms that make up a given amino acid) is referred to as ‘Ti’ .By varying the magnitudes of the parameters σThe actual hydrophobicity distribution resulting from the inter-amino acid interaction (which depends on the distance of the interacting residues and on their intrinsic hydrophobicity) is, to a varying degree, aligned with the idealised distribution expressed by the 3D Gaussian function . The detij is the distance between the positions of the interacting amino acids, c is the cutoff distance, and Hr is the intrinsic hydrophobicity. The value of the observed level of hydrophobicity (assigned to the position of the effective atom) is referred to as ‘Oi’.where rThe first factor in both expressions introduces the normalisation of the distributions. The T, O and R profiles are shown in Fig. Quantitatively, the differences between the idealised distribution, T and the actual distribution, O can be assessed using divergence entropy 3\documenKL value cannot be interpreted quantitatively. Therefore, a second reference distribution, R was introduced, where Ri = 1/N− N is the number of amino acids in the chain.The DThe R distribution, in contrast to the T distribution, represents a state with a uniformly distributed level of hydrophobicity (there is no hydrophobic core).KL(O|T) and DKL(O|R), the ‘proximity’ of the two distributions being compared can be determined. The relation DKL(O|T) < DKL(O|R) indicates the presence of a hydrophobic core. To avoid using two values to describe the same object, the quantity ‘RD’ (Relative Distance) is introduced:By comparing the values of DAn RD value < 0.5 indicates the presence of a hydrophobic core Fig. B.The deviations (the O distribution versus the T distribution) identified in protein distributions can be localised, where single and easily identifiable residues show a deficit or excess of hydrophobicity, as the case may be. If located in the common region of the protein molecule, residues with a hydrophobicity deficit in most cases are components of the active centre. Hydrophobicity deficits are often cavities ready to bind the ligand or the substrate (in the case of enzymes) . A localThe aqueous environment is not the only environment for protein activity.Max is the maximum value for the 3D Gaussian distribution and n is the normalisation of the resulting distribution.Membrane proteins exhibit activity in a hydrophobic environment. Their stabilisation in this environment is ensured by the exposure of the hydrophobic residues to the outside (providing a preferable system with the hydrophobic membrane). If, in addition, the protein acts as an ion channel it has a concentration of polar residues in the central part (in particular). It is therefore an ‘inverted’ system in relation to proteins active in aqueous environments. Therefore, the idealised hydrophobicity distribution for the membrane environment is expressed by the complement of the 3D Gaussian function according to the equation below:As previous analyses showed, the proteins do not demonstrate an arrangement that follows the given distribution Eq. . It appeKL (O|M) value . This parameter expresses the strength with which a given environment modifies the system resulting from the polar water environment. The proper K value for particular set of T and O profiles is found as expressing the lowest Dlue Fig. C. FinallMore on membrane protein structure analysis is provided in , 49.The graphical visualisation of the model in question Fig.  illustraThe M distribution is therefore considered to be an expression of the effect of the environment on the protein folding process, in which the protein adapts to the conditions imposed by the environment.If a juxtaposition of the distributions Fig.  represeni and Oi. A multiple step-wise elimination of these positions allows the identification of that part of the protein that exhibits RD < 0.5, thus identifying the part of the protein with a micelle-like organisation responsible for the solubility of the protein in question.It is also possible to perform an operation to eliminate the positions with the greatest differences between TThe summaries of successive editions of the CASP project show a split between ‘easy’ and ‘hard’ proteins. This distinction applies to all calculation techniques used by the participants. Traditionally, a high degree of prediction difficulty is associated with the presence of a beta-structure, which is more challenging (‘harder’) for obvious reasons (a long-range interaction). However, there are examples of proteins entirely representing the helical structure with a low prediction accuracy score. The search for an answer to the question posed earlier as to why very good force fields that provide models with a high degree of similarity to the target fail for other proteins is done based on the identification of the environmental differentiation of protein folding conditions.The potential user has two possible ways to access the program:The program allowing the calculation of RD as well as T and O distribution is accessible upon request on the CodeOcean platform:https://codeocean.com/capsule/3084411/tree. Please contact the corresponding author to get access to your private program example.https://sano.science) and running on resources contributed by ACC Cyfronet AGH.The application—implemented in collaboration with the Sano Centre for Computational Medicine (https://www.cyfronet.pl) in the framework of the PL-Grid Infrastructure (https://plgrid.pl)—provides a web wrapper for the abovementioned computational component and is freely available at https://hphob.sano.science.. The evaluation criterion used in the current work is the parameters of the FOD model Table .Table 1VThe TARGET column: the parameter values characterise the structure that is available in the PDB. According to the interpretation based on the FOD model, the RD value reveals the degree of organisation of the hydrophobicity distribution against the micelle-like distribution. The K parameter indicates the contribution of non-aqueous factors that affect the formation of the structure of the protein in question.The BEST MODEL column represents the status of the structure of the model by assessing the extent to which the T distribution is reproduced by the O distribution as represented by the model structure.The correlation coefficient: the relationship of the GDT_TS rating value to the status of all models expressed by the RD parameter values.The interpretation of the respective sets as given in the columns is as follows:In the presented system, T0953s-D3 shows a structuring consistent with a micelle-like distribution . The structure of this protein is distinguished by the presence of a hydrophobic core and a polar surface. The contribution of non-aquatic factors is negligible. The values describing the status of the model ranked at the top of the GDT_TS classification also show a high adaptation to a micelle-like system, albeit significantly lower than the target status. This difference turns out to be significant, as the model is very poor in the assessment, despite its top position on the ranking list.The model structure obtained for the target T1065s2-D1 turns out to be very close to that of the protein. Here, the best model scored very highly on the GDT_TS scale. The assessment based on the RD and K parameters also shows considerable similarity.The target T0990-D3 shows structuring above the cut-off level (RD = 0.5) to a small extent (K = 0.5). The status of the proposed model appears to represent a distribution further away from the micelle-like system . This difference results in a relatively low rating on the GDT_TS scale.The target T1024 shows the highest variation against the top model in terms of RD and K. The top model was ranked with a relatively high GDT_TS value (63.3).The meaning of the ‘Correlation Coefficient’ column Table  is shownThe classical GDT_TS rating reduction relationship for RD values moving away from the target status is shown by the model obtained for the target T0953s2-D3  in the proposed model contributes too little to the hydrophobic core.To make the list of discussed examples complete, the structure of target T1065s2-D1 described by parameters: RD = 0.594 and K = 0.5 appeared to be very well predicted with GDT_TS score = 98.47. This best model for this target represents the structure of the status expressed by parameters RD = 0.578 and K = 0.5. This example proves applicability of RD and K parameters as possible criteria for structure comparison.This analysis demonstrates the dependence of the result obtained (the top position on the ranking list) on the RD value of the target structure.Streptococcus agalactiae (PDB ID—6POO – target T1030 in CASP 14) [An extreme case of a globally distinct structure is a pathogenicity protein, putative from Streptococcus agalactiae serotype V from the CASP 14)  The protein entirely represents an example of a helical protein with a structure far from globular. Therefore, a hydrophobic core is not expected to be present here. The structure of the protein (target) is described by the values: RD = 0.786 with K = 1.7 and, to determine the status of distinguished domains: (1–154) RD = 0.674 and K = 0.8, while for the domain (155–273), RD = 0.658 and K = 0.6.High RD values indicate a structuring devoid of a hydrophobic core, while the K values suggest a significant contribution of factors that are not water, driving the structuring. A compilation of the T, O and M distributions for the protein in question shows a nearly linear distribution (similar to the R distribution). This type of distribution is interpreted to be a result of conditions where there is no impact of an aquatic environment. The closer to the horizontal line the M distribution is, the lower the influence the aqueous environment has on the structuring. A protein with this type of M distribution can be thought of as a protein folding effect in a specific ‘water void’ environment.A summary of the results Table  shows thAn example that also requires a detailed discussion is the unknown function target category protein, T1029 (PDB ID—6UF2 ).Complete with respective 3D presentations. Highlighted in red – fragments showing a hydrophobicity deficit; blue – hydrophobicity excess, as shown in the diagrams.The selected example represents the model with the lowest GDT_TS score in the set under discussion Fig. . The parThe model proposed in a group of programs whose history began with the ROSETTA program  is repreThe results of the evaluation of the models obtained with this server are presented in Fig. Analysis of the set of results Fig. A shows aThe summary in Fig. Of particular note are two structures  at RD (model: 0.522)  (PDB ID 6F45). The RD value for the target is 0.286 (extremely low) with K = 0.1, while the structure predicted by T0953s2TS368-D3 shows a structuring with RD = 0.599 and K = 0.5. This is an example of a structure determination by a force field, whose parameterisation is focused on the range of 0.5 < RD < 0.7, as shown earlier. Here, the structure of the model could probably be reproduced by running simulations of the protein folding process in an aqueous environment that directs the process towards a micelle-like form .The vast majority of programs in the ab initio category as well as the recently introduced AI-based method are driven by parameterisation resulting from the analysis of protein structures available in the PDB. The proteome of a particular organism is made up of proteins performing all the biological activities required to sustain life. Given the complexity of the system that is the world of living organisms, it is not surprising and indeed seems necessary to have a high degree of diversity in the tools that are proteins. Structural differentiation stems from the differences in amino acid sequences. In addition, the environment in which a protein performs its function is also a source of high diversity in the protein world. The environmental diversity has a very wide range, which provides a highly differentiating factor for the structures obtained. Therefore, a force field that does not consider external conditions or refers to ‘averaged’ characteristics remains deficient in relation to the great variety of tools and machines that are the proteins in every living organism.The characterisation of the target T0953s2-D3 (CASP13), the structure of which no method could handle, shows a very low value of the K parameter = 0.1 in the native structure. This means that the force fields applied (which presumably are averaged) cannot reproduce the structure according to the micellization mechanism. As shown in Fig. The need to consider environmental specificity for the folding protein is revealed by all amyloid proteins, whose change in structure is not caused by mutation but, it seems, solely by environmental effects.The proposal to include a structure assessment in the RD and K category was already raised . StructuA summary Fig.  reveals The summary of results Fig.  reveals In conclusion: the protein structures do not follow the averaged model. The differentiation of force fields expressing the external conditions shall be present in models oriented on protein structure prediction.The proteins presented in this analysis were selected subjectively, focusing mainly on examples that show significant differentiation in performance between groups and examples that score low, despite the use of models that perform very well for other targets.The analysis of two examples with extreme RD values (0.286 and 0.918), whose structures were predicted to have RD values of 0.599 and 0.783 , demonstrate the validity of the thesis assumed here about the necessity of the environmental factor.As long as protein structure prediction-oriented programs do not take into account the presence of environmental conditions, the prediction is only reliable for a limited group of proteins (here defined by parameters based on the FOD model at 0.5 < RD < 0.7). The use of an averaged parameterisation determined from the analysis of known structures cannot lead to the prediction of protein structures, which are themselves differentiated as products of the contribution of the environment directing the protein folding process.This work proposes a model that takes into account the environmental specificities to be considered in the construction of the external force field.An external field for successive values of K (from K = 0 to K = 1.0 and even K > 1.0) applied to an energy optimisation procedure extended by optimisation due to the presence of an environment that directs the protein folding process appears to be necessary. An unambiguous example is amyloid proteins, which, with an unchanged sequence, acquire very different structural forms depending on the environment . These conclusions are also confirmed by the WeFold project, where combining the tools of the leading groups did not significantly change the validity of the results obtained . The exa


Protein is one of the primary biochemical macromolecular regulators in the compartmental cellular structure, and the subcellular locations of proteins can therefore provide information on the function of subcellular structures and physiological environments. Recently, data-driven systems have been developed to predict the subcellular location of proteins based on protein sequence, immunohistochemistry (IHC) images, or immunofluorescence (IF) images. However, the research on the fusion of multiple protein signals has received little attention. In this study, we developed a dual-signal computational protocol by incorporating IHC images into protein sequences to learn protein subcellular localization. Three major steps can be summarized as follows in this protocol: first, a benchmark database that includes 281 proteins sorted out from 4722 proteins of the Human Protein Atlas (HPA) and Swiss-Prot database, which is involved in the endoplasmic reticulum (ER), Golgi apparatus, cytosol, and nucleoplasm; second, discriminative feature operators were first employed to quantitate protein image-sequence samples that include IHC images and protein sequence; finally, the feature subspace of different protein signals is absorbed to construct multiple sub-classifiers via dimensionality reduction and binary relevance (BR), and multiple confidence derived from multiple sub-classifiers is adopted to decide subcellular location by the centralized voting mechanism at the decision layer. The experimental results indicated that the dual-signal model embedded IHC images and protein sequences outperformed the single-signal models with accuracy, precision, and recall of 75.41%, 80.38%, and 74.38%, respectively. It is enlightening for further research on protein subcellular location prediction under multi-signal fusion of protein. Subcellular proteomics emerged as a unique area of proteomics in eukaryotic cells with the advent of organelle separation technology and proteomics technology, which are conducive to analyzing protein composition, determining the role of subcellular structures, and researching physiological or pathological environments ,2. In coAutomated classification models for predicting protein subcellular locations have been around for a while. They were divided into two groups based on various protein patterns: one is based on protein sequence assembled by 1D amino acid; the other is directed to 2D microscopic images. Both categories work on the workflow of three-step pipelines: data preprocessing, extracting discriminative features that distinguish protein subcellular location, and applying an effective classifier to fit data distribution .One major pipeline in the sequence-based method is that discriminative features were extracted to explore the correlation of 20 natural amino acids between similar protein sequences, including hand-crafted features based on traditional statistical algorithms and abstract features from representation learning. Some feature extraction methods based on the composition of amino acids were defined to express correlation between amino acids: such as k-mer frequencies, Position-Specific Scoring Matrix (PSSM), and Pseudo-Amino Acid Composition (PseAAC) ,8,9. On Two-dimensional microscopic images have grown in popularity with the advent of microscopic technology since they can show the spatial distribution of proteins in various tissues . SimilarIn this paper, we reported a dual-signal fusion system for predicting protein subcellular location by incorporating IHC images into protein sequences, and some key points can be summarized as follows. An important point of all is that a benchmark dataset with a common subcellular location of protein was collected and generalized as the experimental objects from the HPA and Swiss-Prot database. Then, several classical feature operators were employed to quantify IHC images and protein sequences, and abstract features derived from deep-learning models were extracted to depict IHC images. Finally, feature spaces from the mentioned dual signal were applied to build multiple sub-classifiers, and the decision label was generated using a centralized voting mechanism. From the experimental results, the dual-signal fusion method based on protein sequence and IHC images can reach 75.41%, 80.38%, and 74.38% in accuracy, precision, and recall, respectively. It outperforms other single-signal models. Furthermore, it also serves as a reasonable reference for subsequent multi-signal studies.https://www.proteinatlas.org/, accessed on 4 May 2022) database was adopted to collect IHC images under tissue cell types according to data criteria. Specifically, there are two criteria for filtering IHC images, i.e., the protein expression level presents the staining level of the protein channel, and the reliability score judged the reliability level for the subcellular localization annotation of protein. Beyond that, the Swiss-Prot database in Universal Protein Resource  was used to collect the protein sequence dataset. The 4722 proteins involved in nine major subcellular locations contained centrosome, endoplasmic reticulum (ER), Golgi apparatus (Golgi), mitochondria, nucleoli, vesicles, cytosol, nucleoplasm, and plasma membrane, initially selected in HPA. Then, a benchmark dataset described as high quality and with the same label was collected from the two databases according to the following steps.Appropriate datasets in the machine-learning models would yield significant influence. In this work, the Human Protein Atlas  software , in whicThe 281 protein sequences underwent redundancy elimination and length constraint by CD-HIT software, which is conducive to enhancing independence between non-homologous protein sequences and improving attribute-class differences for describing multiple feature information. The CD-HIT is a greedy algorithm. First, the longest of all protein sequences was considered the clustered representation. Then, common word counting is used to describe the similarity and redundancy of the remaining protein sequences.In the protein sequences, some discriminative statistical operators were employed to quantify statistical properties of residues, as shown in ormation ,47,48,49After the above feature extraction, the feature space of the protein sequence was constructed to fit the sub-classifier. To avoid feature redundancy and model overfitting caused by too high dimension, the Least Absolute Shrinkage and Selection Operator (LASSO) was adopted in protein sequence features space . The selSome image-based models have been demonstrated to be advanced in predicting protein subcellular location by extracting shallow and abstract features ,34,35,36There are 281 IHC images that meet the staining level and match 281 protein sequences. In order to improve information richness and focus on the protein-target regions of IHC images, the 15 protein channel patch images were split from one RGB IHC image following the steps presented in In the protein channel patch images, the shallow and deep features were extracted from the statistical methods and deep-learning models to express the attributes of protein regions, as shown in First, the shallow feature composed of Haralick and LBP features was extracted from protein channel patch images. The Haralick feature is a statistical feature based on discrete wavelet transform under GLCM. Protein channel patch images were scaled to 32 gray levels and decomposed down to 8 levels by discrete wavelet transform using the Daubechies filter . Then, tSecond, the abstract features were gained by feeding 15 protein channel patch images into different layers of the deep-learning model, and the average value of 15 vectors was calculated to represent the original image. A CNN composed of residual units and interactive pairwise attention modules was employed to extract abstract features ,55. DiffIn this paper, a sample containing a protein sequence and one IHC image is described via different discriminative quantization methods. Therefore, multiple sub-classifiers were produced after the above process. To further obtain the final output results, the centralized voting mechanism was employed to determine the subcellular location of protein. Specifically, the confidence that each protein belongs to four subcellular locations was evaluated by multiple sub-classifiers; then, the most reliable location derived from selected sub-classifiers would participate in the vote; finally, the subcellular location with the most votes was viewed as the decision result.According to the mentioned methods, two protein signals are represented via multiple discriminative feature operators. The entire experimental process can therefore be divided into three parts: the experimental results of protein sequences in statistical features, the experimental results of IHC images described by shallow and abstract features, and the decision performance for multiple classifier integration. Beyond that, the five-fold cross-validation strategy is utilized to produce more general experimental results.The primary protein structure, which resembles the natural language, shows the chain structure of the protein sequence through the condensation and dehydration of various amino acids. Therefore, in describing the properties of protein sequences, 18 PSSM-based features are employed to characterize amino acid evolution, PseAAC features with 420-dimension are adopted to describe the properties of pseudo amino acids, and PC features with 3675-dimension are used to describe the physicochemical properties and similarity of amino acids. Moreover, to weaken the redundancy of features and avoid overfitting problems caused by excessively high feature dimensions, subset features were selected from original features by LASSO. Next, the subset features were fitted to a high-dimensional category space by the BR classifier with the Radial Basis Function (RBF) kernel function. In this section, 281 protein sequences were subjected to five-fold cross-validation, and the average value of five experimental results is taken as the output result. After the mentioned process, two graphs show the performance of individual features and concatenated features in It can be seen from The image-based system on protein subcellular location and protein-target regions of IHC patch images are conducive to weakening blank areas in the images and increasing the receptive field of feature operators ,36. TherHaralick features were calculated by discrete wavelet transform using db1-db10 Daubechies filters. It can be seen from Next, the fusion features that include feature maps at different depths of CNN and shallow features were applied; the experimental results are shown in So far, according to dual signals composed of protein sequence and IHC images, each protein sample has been digitally characterized from various features, so multiple sub-classifiers have been established. Therefore, to produce a final result, the centralized voting mechanism was employed to assemble multiple confidence of some predictors with better performance to obtain predicted labels. Here, five classifiers were adopted, i.e., the first is based on DP-PSSM and PC features of protein sequence; the second is based on C3 features of IHC images; the third is based on GAP features of IHC images; the fourth is based on C3, Haralick, and LBP features of IHC images; and the fifth is based on GAP, Haralick, and LBP features of IHC images; the results are shown in The experimental results from multiple sub-classifiers obtained can effectively improve the assessment indicators of the model by the centralized voting mechanism. It is undeniable that the results of five-fold cross-validation have fluctuated violently in the existing sample category distribution, according to the box plot in In this study, we developed a dual-signal model composed of protein sequences and IHC images to analyze protein subcellular location patterns. One contribution of this work is that a benchmark dataset with the same protein subcellular location label was collected from the HPA and Swiss-Prot databases and provides a dataset basis for following multi-signal studies of protein subcellular location. Additionally, according to different protein presentation patterns, corresponding feature extraction operators were employed to quantify and characterize protein samples. Specifically, 18 PSSM-based features, PseAAC features, and PC features were adopted to represent protein sequences. Moreover, Haralick features, LBP features based on statistical methods, and abstract features derived from the different depths of CNN were adopted to quantify IHC images. Specially, due to the quantitative differences of different protein signals, LASSO and SDA were applied to protein sequences and IHC images to reduce the feature dimensions to avoid dimension curse and overfitting. Finally, the output confidence of multiple BR sub-classifiers is absorbed to output confidence of protein at the subcellular location, and the centralized voting mechanism was employed to obtain the decision result. The experimental results show that the prediction performance of the dual-signal model based on protein sequence and IHC images can reach 75.41%, 80.38%, and 74.38% in accuracy, precision, and recall, respectively. It proved that the dual-signal model is advanced by incorporating IHC images into protein sequences. However, there is a clear data imbalance in the benchmark dataset from the Swiss-Prot and HPA databases, thus data equalization processing and discriminative representation will be the next research work.


Aptamers are synthetic nucleic acids that are developed to target with high affinity and specificity chemical entities ranging from single ions to macromolecules and present a wide range of chemical and physical properties. Their ability to selectively bind proteins has made these compounds very attractive and versatile tools, in both basic and applied sciences, to such an extent that they are considered an appealing alternative to antibodies. Here, by exhaustively surveying the content of the Protein Data Bank (PDB), we review the structural aspects of the protein–aptamer recognition process. As a result of three decades of structural studies, we identified 144 PDB entries containing atomic-level information on protein–aptamer complexes. Interestingly, we found a remarkable increase in the number of determined structures in the last two years as a consequence of the effective application of the cryo-electron microscopy technique to these systems. In the present paper, particular attention is devoted to the articulated architectures that protein–aptamer complexes may exhibit. Moreover, the molecular mechanism of the binding process was analyzed by collecting all available information on the structural transitions that aptamers undergo, from their protein-unbound to the protein-bound state. The contribution of computational approaches in this area is also highlighted. Intermolecular interactions represent key events in all biological processes. In living organisms, partnerships between biomolecules are characterized by high specificities and a wide range of binding affinities . Proteinhttps://www.rcsb.org/ accessed on 9 November 2023). This search led to the identification of 435 PDB entries potentially containing three-dimensional structures of aptamers. The list of the PDB codes is reported in An initial ensemble of structures containing aptamers was generated by interrogating the RCSB PDB (release of 31 July 2023) using the term “aptamer” as a query in the search box of the website , is reported in https://www.ebi.ac.uk/pdbe/pisa/, accessed on 9 November 2023) applying the default parameters and settings. Linear regression analyses were performed to gain insights into the relationships between parameters . The significance of the correlation coefficients  is expressed with the p-value. The interface area (IA) and the number of direct (not mediated by water molecules) hydrogen bonds (H-bonds) present at the protein–aptamer interfaces of each structure of the dataset were computed with the PISA program  availablThe first structural characterization of a protein–aptamer complex  dates baversus 15). This observation suggests that the structural biology of protein–aptamer complexes is anticipating trends that are expected to occur in a few years in the entire field. Indeed, while in the period January 2022–July 2023 the total number of structures solved by X-ray crystallography deposited in the PDB almost doubled those determined by cryo-EM , it is commonly believed that this scenario will be reversed soon [However, a global analysis of the structural data that emerged in the last three decades indicates that a rather limited number of successful structural characterizations were reported up to 2008 . A signised soon ,124.The analysis of the main features of the structures determined by using these different methodologies reflects their intrinsic specificities. Indeed, structures investigated by X-ray crystallography generally present higher resolution and better accuracy than those determined by cryo-EM b. This i2). In line with a previous analysis conducted on a significantly smaller database [p-value < 10−5)   a. The co < 10−5) , the R-v < 10−5) b. It musIn general, the different protein–aptamer interactions inspired numerous analytical detection assays, each based on the regulation of a specific binding force .2) were observed for RNA aptamers  of electrostatic interaction in the lysozyme–aptamer interface [Notably, most of the structures endowed with the smallest IAs are formed by either short RNA aptamers (< 25 nucleotides) or small proteins (< 100 amino acid residues). These include the small aptamers involved in interactions with viral capsid subunits: (i) F6, F5, F7, and F5/2AP10, targeting the bacteriophage MS2 coat protein  ,46,51; a. In parnterface .Gln var-AGGU bound to glutaminyl-tRNA synthetase  [2) are exhibited by two classes of aptamers: (i) DNA aptamers interacting with HIV-1 reverse transcriptase subunits  [The largest IA is present in the structure of the complex of aptamer tRNAID 1EXD) . Large and 7Z2G) ,108,109 nd 7Z2G) . In all nd 7Z2G) . It is wInterestingly, the analysis of the protein residue types that are present at the protein–aptamer interface in selected and representative structures  is indicMost of the complexes contain a single protein and aptamer chain (protein/aptamer ratio 1:1) . This clA special case in this class is represented by the aptamers bound to the DUX4 protein  . In detaThe NMR structures  ,67 of thTwo distant monomers of the same protein (interleukin-6) are linked by an aptamer chain in entry #52 (PDB ID 4NI9)  eventualMonomeric proteins may be simultaneously targeted by two aptamers, thus forming ternary complexes  [In addition to the previously mentioned  exosite nd 5EW2)  or NU172nd 5EW2)   . Interesnd 5EW2) ,138. Indnd 5EW2) ,138.Recently, a TBA variant (named TBA-NNp/DDp), in which the 3′ and 5′ ends were, respectively, conjugated with two electron-rich  and two electron-deficient  moieties, was studied . The X-rFinally, in the last year, the cryo-EM structures of two 1:2 ternary complexes   in whichSeveral structures of protein–aptamer complexes involving homomeric proteins have been reported over the years. Homodimeric proteins were shown to interact with either one (protein–aptamer 2:1) or two (2:2) aptamers. Both chains of the homodimer are simultaneously involved in the interaction with a single aptamer molecule in the structures of (i) K1 and K2 aptamers bound to the Tet repressor protein  , (ii) A4On the other hand, structures of 2:2 complexes in which each aptamer targets only one chain of the homodimer involve (i) tJBA8.1 aptamer bound to transferrin receptor protein 1  , (ii) anFor higher-order homomeric proteins, the following structures were reported ,68,86,99A comparison of the redundant protein–aptamer interfaces, which are present in the same biological assembly, in complexes 2:2, 4:2, and 6:2 indicated that the interfaces are essentially preserved .Aptamers have been developed also to target large assemblies ,140. A gThe first class comprises aptamers binding viral capsid subunits. Two distinct viruses have been so far targeted and structurally characterized. In particular, three studies were focused on the bacteriophage MS2 that was targeted by the RNA aptamers F5, F5/2AP10, F6, and F7  ,46,51. A2. The global interface formed by this aptamer with the protein/DNA assembly is one of the largest present in the PDB.A completely distinct example is represented by RNA aptamers targeting RNA polymerase proteins. This group was characterized in two structural studies ,118. Theper se not sufficient for understanding the physico–chemical basis of the recognition process. Indeed, the characterization of the intrinsic structural/dynamic properties of the interacting partners is an important step for the elucidation of this process. Indeed, although, for the protein partner, minor global structural modifications are expected and detected, aptamers may undergo significant structural rearrangements upon complex formation. Unfortunately, due to the intrinsic flexibility of aptamers, their characterizations in ligand-free states are not frequent. To comprehensively address this issue, in addition to the dataset of structures of protein–aptamer complexes, we also interrogated the PDB, looking for aptamers that were not bound to proteins. This was carried out by selecting in the PDB the aptamer structures for which the “Polymer” keyword only contained the “Nucleic Acid” expression. Using this approach, we retrieved an ensemble of 291 entries. The sequences of the aptamers of this dataset were systematically compared with those present in the dataset of the complexes. The results of this comprehensive comparison are reported in Molecular recognition is a complex process whose quantitative interpretation requires a deep understanding of the many factors involved. The knowledge of the interactions formed in adducts or complexes is In the case of DNA aptamers, several protein-bound aptamers were shown to share significant sequence similarities with those present in the ensemble of the protein-unbound ones . In mostFor RNA aptamers, although protein-bound and -unbound aptamers frequently share highly similar fragments, the occurrence of the same aptamer in the two ensembles is a quite rare event . Using aA global analysis of these four cases was suggestive of different responses of these aptamers to protein binding . Indeed,In more detail, the binding of the aptamer to the protein S8 was associated with remarkable changes in the RNA structure that led to a novel combination of nucleobase interactions  Figure . The chaAs for the protein S8, the targeting of NF-κB was also associated with remarkable changes in the aptamer structure. In particular, the comparison of the protein-unbound structure of this aptamer , which wFor the pairs (bound/unbound states) characterized by limited variations of the aptamer structure upon target recognition, such as in the case of the prion peptide , this maOn the other hand, Fab–aptamer interactions deserve a specific description. The intrinsic flexibility of aptamer structures makes the experimental characterizations of their ligand-free state extremely complicated. One of the most ingenious strategies developed to overcome this problem was proposed by Piccirilli and coworkers . It consThe comprehensive and systematic analysis of the entire structural content of the PDB here reported revealed that the number of protein–aptamer complexes has been rapidly increasing in the last couple of years. This was essentially due to the possibility of studying these complexes by taking advantage of the impressive methodological and technical advances of the cryo-EM methodology. If this trend continues in the next years, a remarkable increase in the structural information related to these systems is expected to be available shortly. Indeed, the possibility of tackling the non-trivial structural characterization of these complexes with two complementary methodological options opens new scenarios in the field. Depending on the investigated system and the structural details needed, the optimal choice could be made by considering that cryo-EM can easily be applied to large complexes and, therefore, to big protein targets, while X-ray crystallography generally provides higher-resolution models.The analysis of the extent of the protein–aptamer interfaces, in line with a recent independent report , indicatAlthough a large variability of the buried surface area was observed, the values found are similar to those detected in generic protein–RNA complexes. The present survey also indicated that the protein–aptamer complexes that have been structurally characterized have different stoichiometry and, sometimes, articulated architectures. Indeed, although monomeric proteins are still the most represented class of proteins in our dataset, the number of oligomeric proteins is progressively increasing . In receIf experimental structural data related to protein–aptamer complexes have been growing in the last years, the characterization of the structural properties of protein-targeting aptamers in their unbound state is still very poor. Although this type of information is essential for the definition of the protein–aptamer recognition process, only a few of these aptamers have been characterized in their protein-unbound state. Nevertheless, depending on the system, the formation of the complex may have either a remarkable or a marginal impact on the protein structure. However, considering the intrinsic flexibility of aptamers that often prevents their structural characterization in the free state, it is likely that protein–aptamer recognition is associated with a significant alteration of the nucleic acid structure. The data here collected on the few aptamers structurally characterized in both the bound and the unbound states suggest that modifications induced by the binding are more likely for RNA rather than for DNA aptamers.Taking into account the wealth of information provided by computational studies performed in this field ,148, thiIn addition to molecular modeling and MD simulations, the recent success of machine learning approaches in the prediction of both protein structures and protein complexes raises the question of whether RNA/DNA folding and the structures of protein–nucleic acid complexes will be accurately predicted in the next few years ,150. How


Protein–protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein–protein interactions and produce high-quality multimeric structural models.Application of our method to the Human and Yeast genomes yield protein–protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3. We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4.The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu. Escherichia coli competitively identified protein–protein interactions while producing accurate multimeric protein structure models of which 39 by now have been confirmed in high-resolution experiments. Other studies applied our method to the minimal synthetic genome syn3.0 [Escherichia coli, the method competitively resolves Human and Yeast protein–protein interaction networks. As novel targets, we identified Human proteins likely to bind the papain-like protease of SARS-CoV2’s non-structural protein 3 (Nsp3). We also obtained models for SARS-CoV2’s spike protein (S) in complex formation with myelin-oligodendrocyte glycoprotein (MOG) and dipeptidyl peptidase-4 (DPP4). Some of the detected interactions have already been experimentally confirmed in recent literature, others provide novel insights into the pathology of SARS-CoV2. Notably, the interaction with DPP4 has been suggested to cause a higher mortality rate of diabetics contracting SARS-CoV2 [Obtaining a complete map of interacting proteins is crucial to decipher the inner workings of living organisms. Among many other roles, proteins act in dynamic collaboration to fulfill biological functions by catalyzing chemical processes. Commonly, interactions are elucidated through a variety of experimental methods , 47 whice syn3.0  and the e syn3.0 . In the e syn3.0 , a web-be syn3.0 , and TMae syn3.0  to detecARS-CoV2  while thARS-CoV2 .com score and Matthew’s correlation coefficient (MCC) is determined with regard to a negative data set of non-interacting protein pairs produced by the SPRING MCC tool and positive data sets derived from each experimental method. The negative data set has been sampled to contain proteins from different subcellular regions. Figure The pipeline’s performance is validated on 20,610 raw protein coding gene sequences from the Human Reference Genome UP000005640) of the UniProt database. This process evaluates ~ 212 million possible pairs to identify the set of interacting protein–protein pairs. Each interaction is ranked by the Z0 of the https://usegalaxy.org. The results are shown in the right panel of Fig. We repeated the same experiment using the Yeast genome UP000002311) to identify protein–protein interaction networks. In total 6045 protein coding genes were parsed through the pipeline evaluating ~ 18 million possible protein–protein interactions using the public Galaxy instance at 1 to idenEscherichia coli genome [A more detailed analysis regarding the prediction performance of the presented pipeline versus experimental methods has been recently published for the i genome . The pipFor all three genomes, our pipeline was able to implicitly identify individual protein sequences and achieve an overall performance which is comparable if not better than existing experimental methods.We next applied the Galaxy pipeline to the genome of SARS-CoV2 which causes a novel severe acute respiratory syndrome and has been declared a pandemic . The SARcontact and Zcom. The two highest ranking interactions were identified for ISG15 (SM-score = 1.11) and ANKUB (SM-score = 1.10). ISG15 has recently been experimentally confirmed as a substrate [Table ubstrate  and ANKUubstrate .Table 1 We also identified Human proteins interacting with SARS-CoV2’s spike protein (S). The top-ranking interaction was found for angiotensin (ACE2/ACE), which is widely known to be the primary receptor for SARS-CoV2 .The second highest ranking model was detected for the interaction with the myelin-oligodendrocyte glycoprotein , a neuro-inflammatory condition that may cause inflammation of the optic nerve, the spinal cord and brain. Recent research has shown that SARS-CoV2 does trigger a relapse of MOGAD .High-scoring models were also generated for dipeptidyl peptidase-4 . These are DPP4 residues K267, R336, R317, and Q344 , we detected several ubiquitin-like substrates of which some have been experimentally confirmed. The method produced a top-ranking model for SARS-CoV’s spike protein (S) and dipeptidyl peptidase-4 (DPP4) in alignment with existing literature. Our method produced novel complex models between the S protein and myelin-oligodendrocyte glycoprotein (MOG). Here two top-ranking binding modes were produced. Experimental exploration will be needed to determine what impact these novel binding sites might play in pathogenicity, immune evasion, and adaptation. The prediction confidence relies on the accuracy of the homology match between templates, the structural fit and a knowledge-based contact potential, providing likely binding modes and interaction partners for further investigation. Only additional experimental validation can determine which or if any of the predicted binding modes occur in nature.A limitation of our method is that it may produce high-confidence models between proteins which are localized in different subcellular regions. Existing literature has shown that such cross-interactions occur in a significant number of cases. In the present work we avoid filtering predicted protein interaction pairs by their corresponding subcellular locations since this would bias the obtained Matthew’s correlation coefficients. Identifying an accurate set of truly non-interacting protein pairs is critical and particularly challenging for the evaluation of protein–protein interactions. Randomly sampling protein pairs across a genome may lead to the inclusion of interacting protein pairs. A more accurate method is to sample non-interacting sets by pairing proteins from different subcellular regions as presented here. Yet another common suggestion is to exclude homologue protein pairs from the non-interacting set all together in order to avoid the inclusion of interacting pairs. This however is not an option due to the nature of the presented method which relies on homology detection to predict protein–protein interactions.Another limitation is that homology modeling does rely on experimental templates. All of the fifteen most confident models derived for Human proteins interacting with Nsp3’s protease rely on four crystallographic complex structures.This pipeline demonstrates the ability to detect interactome networks for a range of organisms. The increasing number of resolved co-crystal structures in the PDB, will continually improve the model quality and coverage over time . Since tAn individual file or a pair of files containing multiple FASTA entries of protein coding sequences. The pipeline will attempt to identify protein–protein interactions within the set of query sequences.Text file containing the list of all Protein Data Bank (PDB)  entry idhttp://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/.PDB70 threading library files as provided by the developers of HHsearch. These files are used to perform single-chain threading and can be obtained from We present a Galaxy pipeline to predict and structurally model protein–protein interactions on a genomic scale. The pipeline takes the following inputs:com scores.Tabular file containing all identified interactions with their corresponding templates and Zcontact and the fraction of inter chain clashes.Tabular file containing the details of the produced multimeric structural models and the corresponding model properties, i.e. SM-score, TM-score, a knowledge-based contact energy term ECollection containing dimeric structural models for each interaction, including the structures of the identified templates from the PDB.Bar chart displaying the prediction accuracy in comparison to experimental results derived from the BioGRID database.The following outputs are generated:https://usegalaxy.org/u/guerler/w/interaction-prediction.This workflow is available at: The presented pipeline utilizes all protein–protein interfaces available in the PDB as a template library for interface homology detection Fig. . The datIn a consecutive step the SPRING Map tool is applied which uses PSI-BLAST  to detecThe pipeline’s interaction prediction logic uses SPRING with HHsearch and TMalign, and was designed to exploit the redundancy of available protein–protein interfaces in order to predict and model novel protein tertiary structures.i, i = 1,2,…) each associated with a Z-score (Zi). The Z-score is defined as the number of standard deviations by which the raw alignment score differs from its mean. A higher Z-score indicates a higher significance and usually corresponds to a better alignment.Initially each query sequence Q is threaded by HHsearch against the PDB70 monomeric template library to identify a set of putative templates  . BioGRIDInitially, the SPRING MCC tool produces a `negative` data set of non-interacting protein pairs by randomly sampling protein–protein interaction pairs from the set of query protein sequences. If a UniProt localization file is provided, the non-interacting pairs can be determined by sampling protein sequences from different subcellular regions. This approach can reduce the false-negative rate of the resulting negative data set.Subsequently, the protein–protein interaction sets identified by each experimental method are considered to be truly interacting, constituting the `positive` data sets for the cross-validation process. Each method is compared to all other methods using the positive data sets and a negative data set of equal size. The resulting Matthew’s correlation coefficients (MCC) are plotted using the Matplotlib library . An examcom > 25, the complex structure is constructed by structurally aligning the top-ranked monomer templates of Chain A and Chain B to all putative interacting frameworks using the SPRING Model tool which utilizes TM-align. The structural alignment is built on the subset of interface residues. The resulting models are evaluated by the recently established SPRING model score [contact is a residue-specific, atomic contact potential derived from 3897 non-redundant structure interfaces from the PDB using the formula of RW [0 is set to 0.01 through a training set of protein complexes to maximize the modeling accuracy of the interface structures. The final model is evaluated for clashes and removed if more than 10% of the resulting C-alpha atom contacts share a distance of less than 5Å between the interacting pair of protein structures.If a pair of proteins, Chain A and Chain B, is deemed to potentially interact e.g. Zel score :\documenla of RW . The wei


Protein representation learning methods have shown great potential to many downstream tasks in biological applications. A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to effective protein representation learning. However, existing protein representation learning is usually pretrained on protein sequences without considering the important protein structural information.In this work, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a graph neural network model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed graph neural network model via a novel pseudo bi-level optimization scheme. We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments, and the enzyme-catalyzed reaction classification into 384 EC numbers, and these experiments verify the effectiveness of our proposed method.https://alphafold.ebi.ac.uk/. The PDB files are available in https://www.rcsb.org/. The downstream tasks are available in https://github.com/phermosilla/IEConv\_proteins/tree/master/Datasets. The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.The Alphafold2 database is available in  A variety of machine learning-based biological tasks heavily rely on effective protein representation learning, which aims to extract rich sequential and structural information of a protein into a high-dimensional vector. The learned protein representation can be used in many downstream tasks, such as protein function annotation , enzyme-Inspired by the remarkable progress of self-supervised learning in other domains, there are a few recent work to perform self-supervised learning for protein from a sequence perspective . These sThough protein structural information determines a wide range of protein properties , how to STrucure-awarEProtein Self-supervised Learning’ (STEPS) method. This method can not only explicitly incorporate protein structural information into protein representations, but also leverage the existing protein language model to enhance protein representation learning. More specifically, we leverage a GNN to model protein structure and propose two novel self-supervised learning tasks to incorporate the distance information and the angle information into protein representation learning. In particular, the GNN model takes the masked protein structure as input and aims to reconstruct the pairwise residue distance information and the dihedral angle information, respectively.To this end, we propose a novel ‘Furthermore, we propose to leverage the available sequential protein language model pretrained on protein sequences (named as protein LM for short) to empower the GNN model via a pseudo bi-level optimization scheme. This optimization scheme aims to effectively transfer the knowledge of the protein LM to the GNN model. The insight is that we identify the relation between the sequential information and the structural information by maximizing the mutual information between the sequential representation and the structural representation. Then a bi-level optimization scheme is devised to exploit the sequential information in the protein LM by leveraging its relation with the structural information in the GNN model. We name this optimization process as ‘pseudo bi-level’ optimization because we update the GNN model in the outer level, but finally keep the parameters of the protein LM fixed in the inner level to avoid distorting the protein LM. Experiments on several downstream tasks verify the effectiveness of STEPS.In summary, we make the following contributions:To the best of our knowledge, we are the first to explicitly incorporate finer protein structural information into self-supervised learning. Two novel self-supervised tasks are proposed to capture the pairwise residue distance information and the dihedral angle information, respectively.We adopt a pseudo bi-level optimization scheme to exploit the sequential information in the protein LM.We conduct various supervised downstream tasks to verify the effectiveness of STEPS.In this section, we first introduce preliminary concepts and some basic notations used in this article.Protein sequence. Each protein L residues in the sequence and L—1 peptide bonds. The protein sequence is mainly composed of 20 different types of amino acids where each unit is commonly named as residue after being joined by peptide bonds.Protein structure. We illustrate an example of protein structure in ijd between residue i and residue j as the distance between the corresponding iψ capture the rotation of the i, respectively. In the protein structure, the dihedral angles iψ are two important attributes for each residue i.Protein structure as a graph. We model each protein as a graph e between two nodes in the graph In this section, we first introduce a protein modeling method using GNN. Second, we present how to use two novel self-supervised tasks to pretrain the GNN model. Finally, we introduce the pseudo bi-level optimization scheme. The overall framework is shown in X and edge features F, and outputs the node representations and the graph representation.We model a protein structure as a graph and adopt a GNN model  to encodith node in the kth layer of the GNN as i and ive denotes the feature of the edge between i and v. Denote the node representation for the ive refers to the inverse of the square of pairwise residue distance.Note that i as:where To further incorporate the sequential information of a protein, we extract protein sequence representation We propose two self-supervised learning tasks to explicitly incorporate the distance information and the angle information into protein modeling. The distance prediction task preserves the pairwise residue distance information and the angle prediction task preserves the dihedral angle information. In this way, the GNN model yields protein representation, which well captures the overall protein structural information.The pairwise residue distance determines the overall shape of a protein backbone and thus determines the function of a protein to a large extent. To this end, we introduce a distance prediction task to encode the pairwise residue distance information into the GNN model.i and residue j as input, and aims to predict the pairwise residue distance between i and j. The intuition for this operation is that the interactions of residues play an important role in determining the diverse functions of protein .γ determines the kernel shape and i as i can be predicted as:where we parameterize In particular, we propose an angle prediction network To sum up, the loss function for the two self-supervised learning can be compactly written as:where x as a protein sample from Yet, directly fusing representations in This relation captures the correspondence between the sequential representation and the structural representation for a certain protein . Note thThis can be formulated as a bi-level optimization problem:Different from the traditional bi-level optimization, we do not update Similarly, the outer level can be solved as:In this section, we first introduce the pretraining settings including the datasets and the training details. Second, we evaluate STEPS on three supervised downstream tasks and compare STEPS with existing SOTA methods. At last, we conduct ablation studies to verify the effectiveness of different components in our method STEPS.Datasets. We sample an independent set from the alphafold protein database  and remove protein sequences, which have more than 25% sequence similarity with the test proteins, forming a size-40 000 pretraining set.Training details. For the GNN model, we set the dimension of hidden representation as 1280 and the layer number as 2 in our experiments. The threshold to determine whether there is an edge between two residues is set as 7 Å, which is consistent with previous study (T = 30 and apply softmax to the output logits. Besides, we adopt the available protein BERT model in us study . For NNαWe use the cosine learning rate decay schedule for a total of 10 epochs for pretraining. We set the learning rate for the GNN model as Downstream tasks. We finetune the pretrained model on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments (artments , and theartments  into 384Baselines. We compare STEPS with two groups of baselines: methods without and with pretraining. Methods without pretraining include:E-value threshold 1e-3 to prevent label transfer from homologous sequences.Blast : a sequeIEConv : it intrMethods with pretraining are:Pre-LM : it adopDeepFRI : this meSTEPS-w/oLM: we pretrain the GNN model in STEPS without considering protein LMs. We add a layer on GNN for finetuning. The initial node representation for GNN is the concatenation of the one-hot encoding of amino acid and the dihedral angle vectors.For the proposed STEPS, we finetune both the GNN model and the linear head. Besides, we use STEPS-H to denote the STEPS with only finetuning the linear head.Training details. For all methods and all datasets, we adopt a cosine learning rate decay with an initial learning rate 1e-4 and train the models for five epochs with the Adam optimizer for a fair comparison.As shown in In this section, we conduct ablation studies to verify the effectiveness of different components in STEPS.We first remove the mutual information from STEPS, denoted as w/o Mutual, and only use the self-supervised learning losses. As shown in To verify the effectiveness of the pseudo bi-level optimization, we remove this part from STEPS and only optimize the GNN model, which is denoted as w/o Bi-level. Besides, we consider alternate optimization and joint optimization between the protein LM and the GNN model. We find alternate optimization and joint optimization do not perform well and are even much worse than without these optimizations, so we do not report these results here. The reason may be the protein LM collapse during updating . As showWe then demonstrate the effectiveness of the two self-supervised learning tasks: the pairwise residue distance prediction task and the dihedral angle prediction task. We remove the angle prediction task from STEPS and denote it as w/o Angle. Similarly, we remove the distance prediction task from STEPS and denote it as w/o Distance. As shown in Protein representation learning methods are mainly classified into two categories: sequence-based methods and structure-based methods. Sequence-based methods model a protein via its 1D amino acid sequence. For example, There are a few studies to perform pretraining on protein sequences . Bepler Bi-level optimization is a special kind of optimization problem where one level of problem is embedded in the other level. Bi-level optimization has been widely used in the deep learning community due the hierarchy problem structure in many applications ,b,c inclIn this article, to effectively capture protein structural information, we investigate a novel structure-aware self-supervised protein learning approach. Along this line, two novel self-supervised learning tasks on a GNN model are adopted to capture the pairwise residue distance information and the dihedral angle information, respectively. Also, to leverage the pretrained sequential protein language model to further improve the representation learning, we propose a pseudo bi-level optimization scheme to transfer the knowledge of the protein LM to the GNN model. Finally, the experimental results on several benchmarks for protein classification show the effectiveness and the generalizability of our method STEPS.


Proteins derived from different resources with different processing technology have different structure and properties, which enables their application in nutrient retention, delivery, improved digestibility and other functionalities. Emerging technologies, micro- and macronutrients, and new protein resources have been exploited for protein-based complex research. This Research Topic aims to provide a platform for researchers to share their state-of-the-art innovations on the preparation, evaluation and application of protein-based complexes, especially those with improved encapsulation efficiency, nutrient retention, anti-nutritional factor removal, etc., compared to currently available work. In addition to emerging technologies, innovative proteins , hydrocolloids, soluble fibers, and ligand receptors are also of great interest. This Research Topic includes the development of innovative protein-based complexes, improved functionality of protein-based complexes, protein-based complexes for nutrient delivery, nutrient preservation of protein-based complexes during food processing and Zhang, Zhao et al.). The binding with quercetin changed the secondary structure of soybean protein isolate, resulting in a partially unfolded and more flexible structure. Meanwhile, modification by quercetin enhanced the foaming and emulsifying capacities of soybean protein isolate . The addition of polysaccharides had an important effect on the properties and functions of protein-based complexes. Chitosan oligosaccharide modification not only delayed the oxidation of myofibrillar protein but also promoted myofibrillar protein to show better solubility, foaming, and foaming stability . Dong et al. found that the addition of octenyl succinic anhydride modified starch (OSAS) improved hydrophobicity and emulsifying capacity of the soy protein (SP)-(-)-epigallocatechin-3-gallate (EGCG) complexes. Emulsion gels is a novel complex that prepared from proteins, polysaccharides and lipids, which can be used as solid fat replacers. κ-carrageenan (κC), high-acyl gellan (HA), konjac glucomannanon (KGM), pea protein isolate (PPI) and sunflower seed oil were used to prepare the emulsion gels . The presence of polysaccharides enhanced the hardness, storage modulus and resistance against deformation of emulsion gel, where PPI/κC system exhibited superior hardness with a similar level of pig back fat.The binding behavior of protein and bioactive compound can affect the functional properties of their complexes. For example, β-carotene improved DPPH radical scavenging activity, foaming capacity, and emulsifying stability of soybean protein isolate (Yan et al. reviewed the application of zein-based nano-systems for the delivery of bioactive compounds. Recent advances on using zein-based encapsulation and protection systems were systematically reviewed and discussed, offering insights and inspirations for future developers. Wang et al. made improvements on whey protein with the aid of hesperidin, to enhance their performances as emulsion stabilizer. The antioxidant capacity of the complex was improved as well. The work offers new thoughts on the combined and synergistic utilization of proteins and natural bioactive compounds.Some authors reported novel application scenarios for various proteins. Sun, Xu et al. revealed that fermented soymilk has antioxidative and anti-inflammatory effect on mice, providing references on the utilization of fermented products of traditional proteins. The in-depth characterization is also important for the investigation of these large biomolecules. The structure-function relationship can be better understood with the help of modern imaging techniques. Sun, Li et al. reviewed the recent advances on the characterization of various biomacromolecules in foods with the aid of small-angle X-ray scattering, summarizing the pros and cons of this advanced imaging technique for unveiling the mystery of various large molecules in food materials.Some of the papers investigated the undiscovered functions of traditional proteins. SC, XF, and XY prepared, checked and revised the manuscript, and approved the submitted version. All authors contributed to the article and approved the submitted version.


There are continuous efforts to elucidate the structure and biological functions of short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms reside more than 0.3 Å closer than the sum of their van der Waals radii. In this work, we evaluate 1070 atomic-resolution protein structures and characterize the common chemical features of SHBs formed between the side chains of amino acids and small molecule ligands. We then develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model and reveal that the types of amino acids and ligand functional groups as well as the sequence of neighboring residues are essential factors that determine the class of protein-ligand hydrogen bonds. The MAPSHB-Ligand model and its implementation on our web server enable the effective identification of protein-ligand SHBs in proteins, which will facilitate the design of biomolecules and ligands that exploit these close contacts for enhanced functions. In addition to these normal hydrogen bonds (NHBs), short hydrogen bonds (SHBs) with R 6. As the proton donor and acceptor atoms reside more than 10% closer than the sum of their van der Waals radii, SHB interactions deviate significantly from simple electrostatic forces, and instead exhibit strong covalent characters that arise from the quantum mechanical delocalization of both the electrons and protons16. For example, when R shortens, the electronic energy surface for shuttling the proton in a hydrogen bond varies gradually from a double-well potential to a single-well potential with a diminishing barrier9. In the limit that R becomes shorter than 2.4 Å, the proton potential energy surface is essentially barrierless. In these cases, electronic and nuclear quantum effects combine to weaken the confinement of Donor-H bond and enable the proton to be shared between the donor and acceptor groups.Hydrogen bonding plays essential roles in mediating the structure, conformational transformation and biological functions of proteins. Canonical hydrogen bonds form from amino acid residues and ligands that contain O or N atoms and the distances between the heteroatoms, R, usually fall within the range of 2.8–3.2 Å20. Low-barrier hydrogen bonds are often observed in the active site of proteins, and hence are associated with a variety of biological processes ranging from stabilizing the reaction intermediates in enzyme catalysis to regulating the binding of antibiotics in bacterial proteins and promoting biological signal transmission29. Since their original proposal17, low-barrier hydrogen bonds have been under extensive investigations although their geometry, strength and functional importance are still under debate36. Conventionally, NMR spectroscopy is widely used for their exploration because the delocalized protons exhibit characteristically downfield chemical shifts and distinct isotope effects when replaced with deuterium37. More recently, advancements in X-ray and neutron diffraction and optical spectroscopy have enabled the direct detection of the position and local environment of protons, providing crucial information on the structure and behavior of low-barrier hydrogen bonds in large proteins36.A notable type of SHBs is low-barrier hydrogen bonds where the proton transfer barrier is comparable to the zero-point energy of an O–H or N–H vibration, which is typically around 5 kcal/mol. It is proposed that the energy barrier becomes sufficiently low when R of a hydrogen bond lies between 2.45 and 2.65 Å and the proton affinities of the donor and acceptor groups are closely matched. In such a compact structure, nuclear quantum effects allow the proton to move freely between the heteroatoms and the hydrogen bond becomes exceptionally strong39. However, unambiguous identification of these compact structures can only be achieved when proteins are resolved at atomic resolution  model to facilitate their identification39. In this work, we focus on hydrogen bonds that connect amino acids and small molecule ligands, such as enzyme cofactors and drugs, in proteins. We will discern the common chemical characteristics of protein-ligand SHBs and develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model for the effective prediction of their occurrence.SHBs constitute approximately 24% of the hydrogen bonds that form between the side chains of amino acids and are frequently observed linking active-site residues and ligands43, we collect 1070 crystal structures of protein-ligand complexes that are refined from X-ray or neutron diffraction experiments and have resolution greater than or equal to 1.1 Å. These atomic-resolution structures allow us to locate the O and N atoms with a coordinate error of 0.1 Å and properly distinguish SHBs from NHBs. After assessing each protein-protein and protein-ligand hydrogen bond, we categorize it as a SHB if its R is within 2.3–2.7 Å, or as a NHB if its R falls between 2.8 and 3.2 Å. In these analyses, we only consider the side chains of the amino acid residues given their frequent occurrence in SHBs (Tables From the Protein Data Bank (PDB)s Tables  and S2, s Tables . This se6. 81% of these SHBs have O as both the donor and acceptor atoms, while the rest form mostly between O and N atoms in the amino acid side chains and ligands , nicotinamide adenine dinucleotide phosphate (NADP) and flavin nucleotides such as flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). These nucleotide cofactors interact with dehydrogenases and flavoproteins and are essential electron carriers in cellular energy transfer and redox processes. For example, Fig. The protein alphabet contains 20 canonical amino acids, among which 11 have polar side chains and are capable of forming hydrogen bonds. As shown in Fig. Compared to amino acids, ligands often have more complex molecular structures and comprise a variety of functional groups that can participate in hydrogen bonding interactions. For example, monosaccharides are aldehydes or ketones with multiple hydroxyl groups, heme contains a porphyrin ring that is often linked to carboxylate groups, and the pyridine nucleotides FAD and NADP are composed of nucleobases, riboses and phosphates. From Fig. entclass1pt{minimaentclass1pt{minima38. The location of the amino acid in a protein-ligand hydrogen bond is fixed at the side chain of the protein.Based on these analyses, we choose 14 input features for the development of the MAPSHB-Ligand model. These include the charge, residue type and heteroatom of an amino acid, and the charge and functional group of a ligand. We further use the acid and base dissociation constants  curve and calculate the area under the curve (AUC) as a comprehensive metric to evaluate the model predictions across all possible classification thresholdsWe then calculate the relative importance scores of the 14 input features and uncover three key factors that determine the prediction of the MAPSHB-Ligand model. As shown in Fig. Asp-Gly-Thr-Asp and Asp-Gly-Thr-Asp-Asn-Asp-Tyr are often involved in SHB interactions with carbohydrates57. In fact, these sequences are located in the calcium and monosaccharide binding loop of the lectin PA-IIL, and are conserved in various PA-IIL-like proteins found in bacteria55. It is worth noting that the dataset comprises only 41 carbohydrate-binding proteins, which represent 3.8% of the total structures. The remaining structures encompass a wide range of protein types, including signaling and transport proteins. Therefore, the MAPSHB-Ligand model effectively harnesses the diverse protein categories and sequence variations present in the training data, enabling it to enhance the prediction capacity beyond the chemical features associated with protein-ligand SHBs. Apart from the three factors discussed above, the other input features combine to give an importance score of 4.7%  and provide researchers with convenient access to analyze these specialized interactions. The combination of the MAPSHB-Ligand and MAPSHB models presents an efficient approach for investigating protein-protein and protein-ligand interactions involving SHBs, especially in cases where the protein structures are of moderate or low resolution. The predictions obtained from these models can serve as additional restraints in the experimental and computational refinement of protein structures, and aid in the elucidation of the structural basis of protein-protein and protein-ligand interactions. The machine learning models can be further refined and optimized with the continuous advancements in the filed of structural biology and the increasing availability of high-quality protein structures. These and other models will enable new engineering strategies to enhance the stability and functions of proteins and facilitate rational drug design efforts to achieve improved efficacy by leveraging SHB interactions as a key molecular mechanism.Is this work, we have examined the top 1% highest quality structures in the PDB and developed the MAPSHB-Ligand model that effectively detects the presence of SHBs formed between amino acid side chains and small molecule ligands. We further integrate this model into a web server . We then modeled the proteins and ligands using the Amber14SB force field60 and the general Amber force field (GAFF)61, respectively, and optimized the complexes while holding the non-H atoms at their positions in the crystal structures. We used three geometric criteria to identify a hydrogen bond: the heteroatoms are O or N atoms; 2.3 Å 58. The 62. The functional groups of ligands were determined from their atomic connections.After collecting 1070 high-resolution structures from the PDB, we added H atoms to the amino acid residues and analyzed the protein-ligand complexes using the Amber 2016 software package63 with an exponential loss function, using 5000 decision trees and a shrinkage of 0.01. We treated the interaction depth as a hyperparameter and determined it through 10-fold cross-validation. Specifically, we randomly divided each balanced dataset into 10 equal-sized subsets, from which 9 subsets were allocated for training the gradient boosting model and the remaining one was used for validation. This process was repeated 10 times with each subset serving as the validation set once. Each training-validation combination was referred to as a fold. In each fold, we trained the model using a candidate interaction depth between 1 and 12, evaluated its performance by applying it to the validation set, and recorded the loss. We then computed the final loss of the candidate interaction depth as the average of the losses obtained from all folds. We determined the optimal interaction depth as the one with the lowest final loss, and proceeded to retrain the gradient boosting model using the entire balanced dataset. The varImp function in the caret package64 was used to calculate the importance scores for each gradient boosting model, and the overall importance scores for the MAPSHB-Ligand model were obtained by averaging over those from the 10 boosting models. The data of the ROC curve were generated using the plotROC package65, and the AUC score was computed using the auc function from the pROC package66. More details of the hydrogen bond analysis and the development and evaluations of the machine learning models are provided in the Supplementary Information.We used the R programming language to develop the MAPSHB-Ligand model. A gradient boosting model was created for each balanced dataset by invoking the gbm functionSupplementary Information.