The development of methods and algorithms to predict the effect of mutations on protein stability, protein–protein interaction, and protein–DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2. Another important process is the binding of biological macromolecules, at which they adopt particular 3D complexes, including those of IDPs, which upon binding form a well-defined 3D structure [binding), i.e., the difference between the free energy of bound and unbound states. Thus, because of their importance for the biological function of macromolecules, ΔGfolding and ΔGbinding were extensively investigated experimentally, and many methods for predicting them were developed [Biological macromolecules, such as proteins, DNA, and RNAs, perform their functions by adopting a particular 3D structure and being involved in a set of interactions. For many proteins, excluding intrinsically disordered proteins (IDPs), the correctly folded 3D structure is needed to prevent them from protease degradation and to form the desired catalytic set of residues, binding interface, and other functionally important structural features ,2. The atructure . Similareveloped ,7,8,9,10folding or ΔGbinding, involving the substitution of wild-type residues to alanine  [The vast majority of the experimental works were conducted to assess the impact of a given residue on either ΔGcanning) ,12,13. Tfolding of wild-type proteins along with single and multiple mutations. In addition, it also provides information about the experimental conditions, such as pH and temperature. ProNIT and ProNAB are databases of experimentally determined protein-nucleic acid ΔGbinding [Here we briefly outline some of the popular databases of experimentally measured thermodynamic quantities related to protein stability, protein–DNA interaction, and protein–protein binding often used by researchers for developing and assessing the performance of new methods for predicting the stability of proteins and their interactions with other proteins and/or DNA. ProTherm ,15 is a Gbinding ,17. BothGbinding ,19. It ifolding and ΔGbinding. Physics-based methods, on the other hand, use principles of thermodynamics and statistical mechanics to predict the effect of mutations on protein stability. However, these methods can account for the complex physical interactions that determine protein stability but require detailed structural information about the protein and are computationally expensive, which makes them non-applicable for genome-scale investigations. In this article, we only deal with fast methods, those using either adjustable parameters or machine learning.There are numerous computational methods available for predicting the effect of mutations on protein stability and binding ,21,22,23For predicting the effect of mutation on protein stability, several methods have been developed, which can be broadly grouped into structure-based methods and sequence-based methods. The structure-based methods use the protein structure information to derive the features for the wild-type and mutant proteins and then predict the free energy change of the protein due to mutation. The list of the most popular structure-based methods includes FoldX , PoPMuSibinding due to the mutation.The protein–protein binding affinity change of a point mutation has also drawn the attention of the research community. Several computational methods have been reported in the literature for the prediction of binding free energy changes due to point mutations. These methods can be classified into physics-based and knowledge-based methods. Knowledge-based/empirical methods are generally fast and hence better suited for genome-level screening applications like FoldX , SAAMBE binding have also been developed. The available methods are fewer than the methods for predicting the change in folding or binding free energy of protein–protein interactions, and they all require structural information. The list is quite short and includes FoldX [Similarly, computational methods for predicting the effect of mutation on protein-nucleic acid ΔGes FoldX , mCSM-NAes FoldX , PremPDIes FoldX , SAMPDI es FoldX , and SAMes FoldX . It is aes FoldX  allows tfolding and ΔGbinding are essential for protein engineering and understanding the effect of natural variants, i.e., SNVs. We argue that these two tasks may require slightly different approaches and methods. Thus, protein engineering requires methods capable of correctly predicting the effect of any type of mutation on either ΔGfolding or ΔGbinding, with the goal of designing more stable proteins or protein–protein and protein–DNA/RNA complexes with better affinity without any restriction on the type of substitution. In contrast, the methods for predicting ΔGfolding and ΔGbinding of SNVs focus on mutations seen in nature, i.e., in the human population. The goal of this work is to provide an assessment of leading predictors with respect to predicting the change in ΔGfolding and ΔGbinding caused by SNV versus non-SNV and how the predictions are affected by the chemical nature of the mutations. It should be mentioned that our investigation sheds light on another aspect of performance assessment, which is different from previous works focusing on the effect of enrichment of destabilizing mutations in the existing experimental databases [The predictions of the effect of mutations on ΔGatabases . Such enatabases ,50. Otheatabases , as wellatabases .Below, we provide the change in the folding and binding free energies distribution in the leading databases for the entire dataset and for SNVs and non-SNVs . It shouThe first observation confirms the previously noticed enrichment of destabilizing mutations, i.e., the vast majority of mutations make the corresponding folding or binding weaker. This is an expected observation since proteins fold toward their lowest folded free energy state. Similarly, the protein-protein and protein–DNA binding free energies are optimized (see ). This oIn terms of amino acid changes present in the database, the results are provided in the The trend of amino acid change is slightly different in the SKEMPI-SEQ-2388  and SKEMSimilar to the S2648 and two SKEMPI datasets, the datasets used for studying the effect of mutation on protein-DNA binding free energy are also dominated by mutations to alanine . In bothIn this section, we benchmark the leading free energy change predictors against the datasets listed above. It must be clarified that the benchmarking is carried out with the sole purpose of revealing the difference in performance between SNVs and non-SNV cases. It is understood that these algorithms were trained on the above-listed databases, and thus their absolute performance should not be evaluated on the same datasets.Lastly, It is to be noted that all the methods for predicting changes in folding free energy or change in binding free energy (protein–protein or protein–DNA) were developed and optimized against a broad range of mutations. Therefore, in this section, we wanted to understand how these methods perform when different types of amino acid mutations/are considered. The following kinds of mutations were considered based on the properties of amino acids: hydrophobic–hydrophobic, hydrophobic–polar, polar–polar, polar–hydrophobic, small–small, small–large, large–large, large–small, aliphatic–aliphatic, aliphatic–aromatic, aromatic–aromatic, aromatic–aliphatic, positive–positive, positive–negative, negative–negative, and negative–positive. In the case of the S2648, SKEMPI-SEQ-2388, and SKEMPI-3D-3775 datasets, only those categories were considered for analysis where the number of cases was more than 10% of the total dataset . Thus, if a given subset had fewer than 265 data points, it was not considered. A different threshold was applied to protein–DNA databases since, in the case of S419 and ProNAB-237, the total number of cases is relatively small compared to other datasets, forcing us to apply a 50% cut-off of the total dataset . The results for the performance of change in folding free energy predictors, change in protein-protein binding free energy predictors, and change in protein-DNA binding free energy predictors are shown in In the case of changes in folding free energy predictors, SAAFEC-SEQ consistently results in PCC ranging from 0.88 to 0.92  and is cfolding and ΔGbinding, are similar as well, except for protein–DNA databases, where ΔGbinding of SNVs does not have as many destabilizing cases as non-SNVs do. The main difference between SNVs and non-SNVs in the corresponding databases is the type of mutation. For instance, SNV cases in S2648 are dominated by hydrophobic to hydrophobic and small to small mutations, while non-SNV cases are dominated by large to small and polar to hydrophobic mutations, whereas SNV cases in both SKEMPI-SEQ-2388 and SKEMPI-3D-3775 are dominated by small–small and polar to hydrophobic mutations, while non-SNV mutations are from large to small amino acids. We see more cases of polar to hydrophobic and small to small mutations in the case of SNV in the S419 and ProNAB-237 datasets, and non-SNV by large to small and polar to hydrophobic mutations. These differences between SNVs and non-SNVs should be taken into consideration when selecting features for machine learning algorithms for predicting the effects of SNVs. Alternatively, if the goal is to develop a method that predicts the effects of SNVs only, only SNV cases should be used for the training set.This article aimed to reveal the differences between SNVs and non-SNVs in terms of their distributions in the corresponding databases and the performance of leading algorithms for free energy change predictions. Three types of databases were considered: the folding free energy changes, the protein–protein binding free energy changes, and the protein–DNA binding free energy changes. It should be mentioned that the first two are much larger than the third one, and therefore the observations made are statistically more meaningful for the first two. The common observation is that SNVs and non-SNVs are almost equally represented in the databases; roughly speaking, 50% are SNVs and 50% are non-SNVs. The corresponding free energy changes, ΔGfolding, and ΔGbinding, we would like to reiterate again that our goal is not to compare their absolute performance but rather to see the difference in the performance of SNVs vs. non-SNVs cases. A comparison of their performance has been carried out in numerous papers of the developers [In terms of the performance of the leading predictors of free energy change, ΔGvelopers ,54,55,56velopers . The comvelopers .The second main goal of this investigation was to probe the sensitivity of the leading predictors with respect to the chemical nature of mutations. This was motivated by the fact that some mutations involve a drastic change in the chemical nature of the mutation site (from hydrophobic to charged amino acid), while others may preserve the overall chemical nature of the site (from hydrophobic to hydrophobic). An algorithm that properly uses such information in the corresponding features is expected to perform equally on such chemically distinctive cases, while if such properties were not used in the algorithm, one should observe different performance (in terms of PCC and MSE). Indeed, as shown above , some alProTherm is a database of experimentally measured thermodynamic quantities for wild-type and mutant proteins ,15. The S2648: The S2648 dataset, originally curated by Dehouck et al. [k et al. , comprisk et al. . For thebinding as a result of mutation from the second version of the SKEMPI database called SKEMPI-2.0 [Using the Structural Kinetic and Energetic database of Mutant Protein Interactions (SKEMPI)  as the mEMPI-2.0 . First, SKEMPI-SEQ-2388: The SKEMPI-SEQ-2388 dataset is a curated subset of the SKEMPI-2.0 database, which has 7085 mutations from 348 protein–protein complexes. The curation starts with purging entries that have a missing temperature or an exact Kd value for wild-type or mutants, followed by considering only single point mutations, resulting in 4827 mutations over 316 protein complexes. Subsequently, 3000 mutant cases from 210 protein-protein complexes involving only dimers were considered, and multimers were filtered out. Then we calculated the average and standard deviation of binding free energy changes for all the mutations with multiple experimental values in the dataset. We removed all duplicated mutations if the corresponding ΔΔGbinding had a standard deviation greater than 1 kcal/mol and collapsed the rest into a single entry if the standard deviation was less than 1 kcal/mol, resulting in 2450 mutations and over 210 complexes. Finally, we filtered out cases where any of the involved protein chains have less than 20 amino acids, resulting in a dataset of 2388 mutations from 200 protein-protein complexes.SKEMPI-3D-3775: SKEMPI-3D-3775 is also a curated subset of the SKEMPI-2.0 database. The first curation step is identical to that for the SKEMPI-SEQ-2388 dataset, and it resulted in 4827 mutations in 316 proteins. In mutation entries where multiple experimental values are listed, the mean and standard deviation of the ΔΔGbinding for the mutation are computed, and if the standard deviation of ΔΔGbinding is greater than 1.0 kcal/mol, the mutant data point is eliminated; otherwise, we consider the average ΔΔGbinding for the mutation, which will be included in the cleaned dataset only once. After this cleaning step, we obtained 4067 mutations from 316 protein–protein complexes. Afterward, cases with missing residues in the −5 to +5 position of the mutation site were also discarded, as this information is required by some of the methods [ methods . Some ofProNIT  and ProNS419: This dataset has been curated by Gen Li et al. [i et al.  and has i et al. . The S41i et al. , and (b)i et al. .ProNAB-237: ProNAB-237 is a subset of the ProNAB database [−1 for a given mutation. In these cases, the average value of the change in binding free energy was taken into account and used for benchmarking. The dataset was further pruned to remove mutations for which atomic coordinates were absent in the PDB. In some PDBs, the mutation residue listed in the database does not map to the residue in it; such cases were also removed, and finally, we have a dataset of 237 mutations. We named this dataset ProNAB-237.database  used in SAAFEC-SEQ : SAAFEC-INPS-MD ,53: INPSI-mutant 2.0 : I-mutanmCSM : mCSM isMAESTRO : MAESTROPoPMuSic : PoPMuSiSDM : Site-DiDUET : DUET isAltogether, eight different methods were used for the benchmarking, three of which are sequence-based, while others require 3D structure as the input for making predictions. Below, we briefly outline these methods. While there are plenty of methods available for the prediction, the following were chosen because they are popular, easy to use, and easy to install:Overall, the following listed computational methods were considered in this work. We could not include methods that are designed for predicting the effects of single amino acid mutations on binding free energy change for dimeric complexes or whose online servers were slow or busy and stand-alone versions were not available or not trivial to install and configure locally.SAAMBE-SEQ : It is aSAAMBE-3D : SAAMBE-https://biosig.lab.uq.edu.au/mcsm_ppi2/ (accessed on 1 May 2023);mCSM-PPI2 : mCSM-PPMutaBind2 : MutaBinhttp://babylone.3bio.ulb.ac.be/beatmusic/index.php (accessed on 1 May 2023).BeAtMuSiC : BeAtMuSThree methods were utilized for benchmarking the binding free energy changes of protein–DNA interactions in the presence of a chemical modification. Both methods rely on a three-dimensional structure for the prediction of the binding free energy of protein–DNA interactions.SAMPDI-3D : SAMPDI-mCSM-NA : The mCSPREMPDI : PremPDIThe SNV cases were extracted from the corresponding experimental databases using the lookup table provided in the Supplementary Material . The resFollowing previous papers, the change of the folding free energy is calculated as the change of the folding or binding free energy produced by a single amino acid substitution as the difference of the folding and binding free energies of the wild type and the mutant ,62. HoweIn contrast, the change in binding free energy for both protein–protein and protein–DNA complexes was calculated asTo avoid a plausible bias toward the selection of data points, we randomly pick up 100 times “N” number of datapoints, where “N” is defined as the minimum of 50% of the SNV or non-SNV cases. For example, in the S2648 dataset, there are 1493 cases of SNVs and 1154 cases of non-SNVs. We pick up the smaller number, 1154 non-SNV cases, and 50% is 572 cases. Thus, 572 cases from the whole S2648 dataset and from subsets of SNVs and non-SNVs are randomly selected 100 times. Then we report the averaged PCC, MSE, and standard deviation. For the analysis of chemically different types of mutations, i.e., hydrophobic to hydrophobic, etc., we first check if the total number of datapoints is greater than 10% of all available data points. If not, the class is not considered. For example, 10% of S2648 dataset is 265 data points . Then we randomly pick up 100 times “K” number of datapoints from the corresponding dataset, where “K” is defined as 50% of the data points . Then we report the averaged PCC, MSE, and standard deviation.ith sample.ith sample.The accuracy of the predictions was assessed using two measures, namely the Pearson correlation coefficient (PCC) and the mean square error (MSE), which are defined below.The paper showed that the distribution of SNV vs. non-SNV types of mutations is different in the corresponding databases of experimentally measured quantities. This should be taken into consideration when one applies methods of predicting the effect of missense mutations seen in the human population. Furthermore, the work indicated that the leading algorithms for predicting folding and binding free energy changes caused by mutations perform differently in cases of SNVs and non-SNVs. This is an important observation since there is a strong linkage between the change in folding and binding free energies and the probability of mutation being pathogenic . Overall


Cross-linking mass spectrometry (XL-MS) is a powerful technique for detecting protein–protein interactions (PPIs) and modeling protein structures in a high-throughput manner. In XL-MS experiments, proteins are cross-linked by a chemical reagent (namely cross-linker), fragmented, and then fed into a tandem mass spectrum (MS/MS). Cross-linkers are either cleavable or non-cleavable, and each type requires distinct data analysis tools. However, both types of cross-linkers suffer from imbalanced fragmentation efficiency, resulting in a large number of unidentifiable spectra that hinder the discovery of PPIs and protein conformations. To address this challenge, researchers have sought to improve the sensitivity of XL-MS through invention of novel cross-linking reagents, optimization of sample preparation protocols, and development of data analysis algorithms. One promising approach to developing new data analysis methods is to apply a protein feedback mechanism in the analysis. It has significantly improved the sensitivity of analysis methods in the cleavable cross-linking data. The application of the protein feedback mechanism to the analysis of non-cleavable cross-linking data is expected to have an even greater impact because the majority of XL-MS experiments currently employs non-cleavable cross-linkers.In this study, we applied the protein feedback mechanism to the analysis of both non-cleavable and cleavable cross-linking data and observed a substantial improvement in cross-link spectrum matches (CSMs) compared to conventional methods. Furthermore, we developed a new software program, ECL 3.0, that integrates two algorithms and includes a user-friendly graphical interface to facilitate wider applications of this new program.https://github.com/yuweichuan/ECL-PF.git. A quick tutorial is available at https://youtu.be/PpZgbi8V2xI.ECL 3.0 source code is available at The online version contains supplementary material available at 10.1186/s12859-023-05473-z. Cross-linking mass spectrometry (XL-MS) is an emerging technology in the field of proteomics that provides insights into protein–protein interactions (PPIs) and protein structures. The significance of XL-MS lies in its ability to detect PPIs that are weak, transient, or difficult to study using other methods such as co-immunoprecipitation or yeast two-hybrid assays. Compared to X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, XL-MS needs less complex sample preparation and provides much higher throughput in the structural study .However, one of the major challenges of XL-MS is the low peptide fragmentation efficiency in the collision-induced dissociation (CID) process. During the process, longer (or heavier) cross-linked peptides often impede the fragmentation of shorter (or lighter) cross-linked peptides, resulting in less informative fragment ions in the MS/MS spectrum , 3. To aWe proposed a protein feedback idea in the cleavable cross-linking data analysis and used the protein-peptide association to help identify insufficient fragmented peptides . This meThe implementation of the protein feedback method to the non-cleavable cross-linking data can make more impact in the field because the majority of XL-MS experiments still uses non-cleavable cross-linkers , 6. In tIn XL-MS, imbalanced fragmentation of cross-linked peptides leads to low-quality XL-MS spectra. Although designing suitable scoring functions can help identify correct peptides, scoring function does not completely solve the problem, especially when no fragmented ions of the shorter or lighter peptide are available. To address this issue, we propose to use additional  information in peptide identification.N (default 20) cross-linked candidate pairs are then cached for examination. Subsequently, we apply a filtering step to retain only the highest-scored candidate and those whose score is at least 80% of the top one.In XL-MS, each query spectrum undergoes preliminary identification of potential peptides using a scoring function. The top The first hit in each spectrum is collected and filtered based on a score cutoff, which is determined empirically and varies depending on the specific scoring function used.The filtered peptides are considered the “true” positives temporarily and are used to build a protein score database. Each of these “true” positives contributes weight to its corresponding protein score .N pairs of each spectrum are re-ranked by their protein scores. And the cross-linked pair with the highest protein score is considered the correct cross-link spectrum match (CSM).After building the protein score database, the top After the adjustment of protein feedback in the peptide-spectrum matching process, the target-decoy approach is used to control the false discovery rate (FDR) .ECL 3.0 is an extension of ECL-PF [Suppose two similar peptides are ranked as the top hits by a scoring function with no preference for one over the other. One peptide has many sibling peptides identified from other spectra, whereas the other does not have any sibling peptides being identified elsewhere. Because sibling peptides from the same protein should have higher chances to appear in other MS/MS spectra than some irrelevant peptides, we propose to add more weight to a peptide with many sibling peptides than a peptide without sibling peptides. We define this adjustment as the protein feedback method . For comf ECL-PF , which wf ECL-PF , a non-chttp://bioinformatics.hkust.edu.hk to help users get started with the software. Additionally, we have created a detailed tutorial with step-by-step instructions for using ECL 3.0, which can be accessed at youtu.be/PpZgbi8V2xI.ECL 3.0 is written in conjunction with Python3 and C++. We provide a testing dataset at Figure We have done three different types of validation using real data sets in , includiDetails of the parameter settings and further experimental comparisons can be found in the Additional file https://github.com/yuweichuan/ECL-PF.git.ECL 3.0 is a comprehensive cross-linking mass spectrometry data analysis tool. It enables us to unravel more intriguing PPIs and protein structure conformations by using the protein feedback mechanism. ECL 3.0 is available with GUI version and is open source at Project name: ECL 3.0https://github.com/yuweichuan/ECL-PF.gitProject home page: https://zenodo.org/record/8176558Project alternative page: Operating system(s): Windows.Programming languages: Python and C++.Other requirements: Python 3.6 or higher.License: MITAny restrictions to use by non-academics: NoneAdditional file 1. Further comparisons, validations, and supplementary figures.


Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery.In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method  on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures.https://github.com/jianlin-cheng/DProQA.The source code, data, and pre-trained models are available at  Proteins perform a broad range of biological functions. Protein–protein interactions (PPI) play a key role in many biological processes. Understanding the mechanisms and functions of PPIs may benefit many areas such as drug discovery  and protRecently, AlphaFold2-Multimer , an end-However, unlike the tertiary structure quality assessment with many machine learning, particularly deep learning methods developed for evaluating the quality of tertiary structural models over many years, there are few deep learning methods for evaluating the quality of quaternary structural models. Existing EMA methods have not leveraged cutting-edge attention-based transformer-like deep learning architectures  to enhanIn general, EMA methods for predicted structures can be divided into two categories: multi-model methods and single-model methods. Multi-model methods take a pool of protein structural models as input and may use a comparison between the models to evaluate their quality, such as in the procedure performed by Pcons , ModFOLDProtein tertiary and quaternary structure prediction. Predicting protein structures has been an essential problem for the last several decades. Recently, the problem of protein tertiary structure prediction has largely been solved by deep learning methods  mechanisms to achieve state-of-the-art results in different domains. For example, the Swin-Transformer achieved state-of-the-art performance in various computer vision tasks , we demonstrate the state-of-the-art performance of DProQA in comparison with the existing methods such as ZRANK2 , GOAP Z and GNN_DProQA was blindly tested in the 15th community-wide Critical Assessment of protein Structure Prediction (CASP15) in Illustrated from left to right in K-NN graph representation. K-nearest neighbors (K-NN) has been used in many previous studies for protein structure analysis and molecular representation learning  is added for each edge. To encode the chain information, a binary feature indicating if two residues associated with an edge are two adjacent (consecutive) residues in the same chain is used. In addition, an edgewise positional encoding -based structure scoring methods , which di and j in the graph. kd is the dimension of the attention head. Finally, the result is element-wise multiplied with k. The original edge feature vector k. Specifically, for each attention head k, the attention coefficients j. These values are then multiplied with a sigmoid output of the gated features of the neighboring node j, after which all these values are summed up over the neighbors of node i. i including itself. The resulting vector is transformed by a learnable matrix i feature vector at level In particular, the GGT adds on top of the standard graph transformer architecture  two infop. This graph embedding p is then fed as input to DProQA’s two read-out modules, where each read-out module consists of a series of linear layers, batch normalization layers, LeakyReLU activation, and dropout layers , e.g. i. N denotes the number of examples in a given mini-batch and C for the number of classes.Overall loss. DProQA’s overall loss is the weighted sum of the two losses above: Multimer-AF2 training dataset. Similar to Docking Decoy Set for training. The Docking Decoy dataset  dataset is the first test dataset. We applied AlphaFold-Multimer  in 2022 CASP15 EMA category. DProQA was evaluated on 36 protein complex targets whose exprimental structures were available for us. Each target contains around 350 models from different CASP15 protein quaternary structure predictors.Training labels. DProQA performs two learning tasks simultaneously. In the regression task, DProQA treats true DockQ scores (Q scores  of decoynatf) for each decoy. We directly used The true DockQ scores of the models in MAF2 and DBM55-AF2 were calculated by using the DockQ tool  to compaBaseline methods. We compared DProQA with three typical methods: ZRANK2, GOAP and GNN_DOVE. ZRANK2 is a method using a linear weight scoring function for evaluating protein complex structures. GOAP score is composed of all-atoms level distance-dependent and orientation-dependent potentials. GNN DOVE is an atom-level graph attention-based method for protein complex structure evaluation. It extracts the interface areas of a protein complex structure to build its input graph.DProQA variants. Besides the standard DProQA model, we also report results on the DBM55-AF2 dataset for a selection of DProQA variants curated in this study. The DProQA variants includes DProQA_GT which employs the original Graph Transformer architecture  rate is defined as the number of protein complex targets for which it ranks at least one acceptable, medium or higher-quality decoy within its Top-N ranked decoys, which is a metric used by the Critical Assessment of Protein–Protein Interaction (CAPRI) ( (CAPRI) . In thisThe results in 3rd. DProQA achieved a 0.200 ranking loss. All single-model methods’ average ranking loss is 0.307. The result of TM-score ranking loss for all the CASP15 multi-model and single-model EMA methods can be found in the DProQA (team: MULTICOM_egnn) participated in the Estimation Model Accuracy (EMA) category of the CASP15 running from May to August 2022. We collected all EMA prediction results from the CASP15 website  and usedCompared to the general high accuracy of predicted tertiary structures , the aveA more common way to evaluate the ranking ability of the quality assessment (QA) methods for protein complexes is the hit rate, a standard method used by CAPRI. However, a hit rate only measures the number of qualified decoys in the TOP N ranked models, without measuring the difference between the best possible model and the top-ranked model. Therefore, in this work, we also apply the loss metric widely used in evaluating the quality assessment methods for protein tertiary structures to the quality assessment for protein quaternary structures.Considering these two metrics together helps us evaluate a protein multimeric QA method’s ranking ability more effectively. For example, ZRANK2 which has slightly better hit-rate performance than DProQA on the DBM55-AF2 set, while its loss is much higher than DProQA’s loss. Overall, DProQA demonstrates consistently good performance on our internal benchmark as well as the most rigorous blind CASP15 benchmark.On the DMB55-AF2 test dataset, DProQ’s average running time of assessing the quality of the models of each target is about 12 s, which is much faster than GOAP and GNN_DOVE but slower than ZRANK2—an energy-based method  and templates. In contrast, DProQA’s prediction is solely based on a single 3D model and therefore provides a fast and complementary estimation of the quality of a structural model.In this work, we present DProQA—a gated graph transformer for protein complex structure assessment. Our rigorous experiments and CASP15 results demonstrate that DProQA performs relatively well in ranking decoy models of protein complexes and the gated message passing in the transformer is useful for improving its performance. Both the tool and the new datasets consisting of multimer models predicted by AlphaFold2 and AlphaFold-Multimer are made publicly available for the community to further advance the field.btad203_Supplementary_DataClick here for additional data file.


Buffer specifications were derived from the literature using named-entity recognition techniques developed using common nomenclature provided by protein biochemists. PurificationDB also incorporates information associated with well-known protein databases: Protein Data Bank and UniProt. PurificationDB facilitates easy access to data on protein purification techniques and contributes to the growing effort of creating open resources that organize experimental conditions and data for improved access and analysis.The isolation of proteins of interest from cell lysates is an integral step to study protein structure and function. Liquid chromatography is a technique commonly used for protein purification, where the separation is performed by exploiting the differences in physical and chemical characteristics of proteins. The complex nature of proteins requires researchers to carefully choose buffers that maintain stability and activity of the protein while also allowing for appropriate interaction with chromatography columns. To choose the proper buffer, biochemists often search for reports of successful purification in the literature; however, they often encounter roadblocks such as lack of accessibility to journals, non-exhaustive specification of components and unfamiliar naming conventions. To overcome such issues, we present PurificationDB (Database URLhttps://purificationdatabase.herokuapp.com/ Protein isolation and purification is a critical step for studying proteins in the fields of biochemistry, drug discovery and structural biology . A succeLiquid chromatography methods can be applied for both analytical and/or preparative goals. In analytical applications, relative proportions of analytes in a mixture can be measured using column chromatography . As a prLiquid chromatography involves the application of a mixture of molecules, known as the ‘mobile phase’, to a surface known as the ‘stationary phase’. The stationary phase dictates which molecules can pass through the column and at which speed they do so based on their physical and chemical interactions with the stationary phase. Molecules are eluted from the stationary phase by competition or by changing their ionic strength through alteration of pH or the addition of salt solution . The mosThe SEC separation is achieved by passing the mobile phase through a packed resin containing pores of different sizes . MoleculAnother commonly applied chromatography technique for protein purification is IEC. The separation principles of IEC are based on electrostatic attractions between charged particles on the surface of the stationary phase and charged areas of the molecules in the mobile phase. The charges of the molecules in the mobile phase and on the resin are balanced by counterions present in salt or buffer ions. When molecules bind to the resin, these counterions are displaced mediating so-called ‘ion exchange’. Molecules are eluted from the resin by increasing ionic strength of the buffer .Affinity chromatography is another method of protein separation that utilizes a protein’s characteristic affinity for specific chemical groups as the principle for separation. Protein affinity tags, which are peptide sequences, are genetically engineered to the N- or C-terminus of the protein of interest. The stationary phase is designed to have functional groups which make a complex with these affinity tags when the mobile phase is passed through the column . One of Although SEC, IEC and affinity chromatography are the focus of this work, there exist other specialized chromatography techniques worth mentioning. Some examples include biorecognition chromatography, which is a type of affinity chromatography based on molecular recognition , and simIn addition to choosing a separation technique, researchers must also consider the composition of buffer solutions based on the application and the characteristics of the protein. In liquid chromatography, buffers are used in several steps of the protocol: as part of the mobile phase and then as an eluent to remove molecules that are interacting with the stationary phase. The general role of buffering agents is to resist changes in pH and maintain protein stability. However, buffers also serve additional roles, for example, in IEC, the elution step requires a higher salt concentration relative to that of the buffer used in the mobile phase, as the salt must compete with the bound proteins to remove them from the resin . Anothera priori. Therefore, biochemists need to consider protein size, type and location of the tag, isoelectric point, presence of disordered regions, solubility of the protein, etc. They often search the literature for reports of successful purification of their protein of interest. Unfortunately, there are many roadblocks to deriving relevant information such as lack of accessibility to journals and their supplementary information and lack of detailed experimental methods in published works. Furthermore, some articles do not fully describe the procedures performed but rather reference other articles, so finding a detailed protocol may require cumbersome surfing through a series of works. Additionally, authors might use different units of measurements and nomenclature conventions, thereby further complicating the retrieval and comparison of various purification protocols. Thus, there is a need for an easily accessible, comprehensive and standardized source of information for protein purification conditions. To this end, we developed PurificationDB, an open-access database that contains curated and focused information on buffer conditions for use in SEC, IEC and affinity chromatography for protein purification.To successfully purify a protein of interest, one needs to calibrate a set of conditions and decide on the components and concentrations of buffer solution. With the abundance of such parameters, it is challenging to estimate the right settings purificationdatabase.herokuapp.com.PurificationDB is a structured knowledge base, developed by automating information extraction from thousands of relevant published articles. We use domain knowledge to write rules for named-entity recognition. Our database contains relevant buffer conditions including pH, concentrations of salts, buffering agents, detergents and other additives. We have also included connections to related databases by UniProt identifiers (UniProt ID) , ProteinHomo sapiens and having the label ‘protein only’ (i.e. protein–nucleic acid complexes were not considered). The PDB codes were then used to retrieve digital object identifiers (DOIs) of the corresponding papers. This yielded 16 204 unique DOIs. Subsequently, we developed a visual automation script using SikuliX to download full texts of these studies  are not included. Additionally, the index of a sentence with a keyword is saved to later infer the order of chromatography steps.After the search space reduction, the next step was to search for experimental conditions within the retrieved sentences. Rules were defined based on linguistic patterns from the literature of this domain as we observed that the language used to describe experimental protocols tends to be quite standard. One example of such a pattern is where the measurement and its unit, if applicable, were placed relative to their respective characteristic or buffer component. The simplest example is the pH value that almost always comes after the word ‘pH’. The chemical component reference table enabled searching for relevant buffer names and concentration values. Importantly, the retrieved concentration values were then standardized so that the resultant database contains easily comparable values.After performing feature extraction of buffer conditions from papers, we connected these conditions to their associated proteins in our knowledge base. To do this, the associated PDB IDs were mapped to the corresponding UniProt IDs using the UniProt database identifier mapping tool , 24. OncThe website interface for accessing the purification database was designed using React for the front end and Node environment for the back-end . Data weOf the original 16 204 fetched paper DOIs, ∼11 700 full texts were successfully extracted. Of those, 4732 papers contained relevant buffer conditions as determined by our search algorithm. The buffer conditions were first classified based on the type of chromatography they were used for. There is a notable difference between the number of buffer conditions for SEC versus for IEC and affinity chromatography .Upon manual investigation of several hundred such examples, we observed that a striking number of papers reported the type of the purification method but did not report the purification buffers. These papers only specify a single buffer used for the final chromatography step. SEC is often the last step of purification before crystallization or functional assays, which is why we see such a big difference between the number of buffer conditions extracted for SEC relative to IEC and affinity .To give the user a better understanding of subsequent experimental steps the buffer was used in, and to determine if it is used as the preparation or elution buffer, we include the portion of the text where the buffer is described in our database.The extracted buffer conditions were then classified based on chemical components and their role in the buffer, i.e. salt, buffering agent, detergent or additive . The varAll the protocols contained information about pH as this parameter ensures that the protein maintains its activity and stability during the purification process. The purity and quality of the protein can be drastically undermined at suboptimal pH. Each protein has a range of physiological pH that is determined by amino acid composition and function. Proteins can withstand fluctuations in pH as they move between subcellular compartments, which have different pH values, to carry out their biological functions . This exThe minimum value of pH in the database is 3, while the maximum is 10.5, and the mode of the pH values was 7.5 . The modMycobacterium tuberculosis peptide deformylase   . Gp160 c P9WIJ3) . Peptidepeptides . This repeptides .All the database entries contain a specification of a salt component of the buffer. Salt is an important component in buffer solutions as it influences the selectivity of protein interactions with the resin and the structural stability of the protein . Salts hIn addition, we mapped the PDB IDs of the proteins from the database to their respective UniProt IDs. Out of 4732 entries in the database, 3877 belong to unique UniProt IDs representing as many unique proteins. We also observed that 239 entries could not be mapped to a UniProt ID, and these mostly represented artificially constructed proteins. Furthermore, we observed that the protein set present in the PurificationDB is very diverse as the proteins belong to 3662 protein clusters defined by UniRef50 sequence clustering . We alsoBesides, we examined the proportion of proteins that were crystallized as one component compared to protein complex structures . We founIn addition to protein–protein interactions, we also investigated the number of entries which contained a protein with post-translational modifications (PTMs) . This inOur user-friendly interface allows for simple browsing and exploration of the protein purification conditions collected from the literature. The ‘Database’ tab allows the user to look through all entries of the database organized by UniProt ID . Upon seThe ‘Search’ tab allows for the user to easily search the entire database by protein name, UniProt ID or sequence. As there are often many names which identify one protein, we developed a reference table which contains all UniProt protein names and associated alternative names for entries in our database. Therefore, upon user entry for the protein name search, this reference table is used to allow for a more robust search of proteins in our database to find the right entry. The protein name search is performed using MySQL’s SOUNDEX function, which converts a string to a code based on how the string sounds when spoken in English and can therefore be used to evaluate the similarity of two strings . This fuPurificationDB is a curated database with 4732 entries of buffer conditions for protein purification using SEC, IEC and affinity chromatography derived from the literature. The derived buffers were used in the preparation procedures for crystallization that yielded protein structures deposited in PDB . Thus, wFurthermore, the database entries are mapped to their corresponding PDB IDs and UniProt IDs along with protein sequence. We retrieved buffer conditions used to purify 3877 unique proteins that represent a broad range of sequence diversity. With this number, we estimate that we capture around 18% of all human protein assuming 21 306 unique proteins in the human proteome . As we rin vitro from the published literature, and PINT, a database of thermodynamic parameters and experimental conditions for protein–protein interactions (PurificationDB is hosted on a user-friendly web interface which allows users to search for entries to easily access conditions for their protein of interest. We developed this database to make information more accessible to researchers, so they no longer have to search for and read through multiple articles to find the experimental protocols for protein purification of their interest. This contributes to the growing effort of creating resources that organize experimental conditions and data for improved access and analysis. Other examples of such resources include LLPSDB, a database containing a collection of proteins involved in liquid–liquid phase separation with corresponding experimental conditions ractions , 47. PurAll entries of buffer conditions that are part of PurificationDB are those that have led to successful purification and ultimately crystallization of proteins of interest as they are derived from entries in PDB. As such, data from PurificationDB may serve as a reliable training dataset for machine learning models or other methods for the prediction of conditions at which new proteins, which do not have published protocols associated with successful separation, may be purified.In future, we hope to expand the ability of our data wrangling processes to collect experimental conditions not limited to a specified vocabulary and language rules for a more robust, through more sophisticated natural language processing techniques. Such development would expand the list of compounds discovered and could ultimately be applied to a collection of other types of experimental protocols.


Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches. In recent years, deep learning has emerged as a promising technique for DTA prediction, leveraging the substantial computational power of modern computers.A critical aspect of We proposed a novel sequence-based approach, named KC-DTA, for predicting drug-target affinity (DTA). In this approach, we converted the target sequence into two distinct matrices, while representing the molecule compound as a graph. The proposed method utilized k-mers analysis and Cartesian product calculation to capture the interactions and evolutionary information among various residues, enabling the creation of the two matrices for target sequence. For molecule, it was represented by constructing a molecular graph where atoms serve as nodes and chemical bonds serve as edges. Subsequently, the obtained target matrices and molecule graph were utilized as inputs for convolutional neural networks (CNNs) and graph neural networks (GNNs) to extract hidden features, which were further used for the prediction of binding affinity.in silico drug discovery, offering promising opportunities for accelerating the drug development process. All the data and code are available for access on https://github.com/syc2017/KCDTA.In order to evaluate the effectiveness of the proposed method, we conducted several experiments and made a comprehensive comparison with the state-of-the-art approaches using multiple evaluation metrics. The results of our experiments demonstrated that the KC-DTA method achieves high performance in predicting drug-target affinity (DTA). The findings of this research underscore the significance of the KC-DTA method as a valuable tool in the field of  Molecule screening plays a pivotal role in drug design by identifying small molecules that have the potential to bind to target protein. However, traditional laboratory experiments for molecule screening are expensive and time-consuming. To overcome these limitations, computer-aided techniques, collectively known as virtual screening techniques, have emeraged. One such technique is molecular docking . MoleculIn some sequence-based approaches such like GraphDTA and DeepDTA, protein sequences need to be truncated due to limitations in neural network inputs, resulting in the loss of crucial information on long sequences. In this article, we proposed KC-DTA, a sequence-based method that ensures the integrity of protein sequences for drug-target affinity prediction. The protein sequences are mapped into matrices using the k-mers and Cartesian product calculation to capture the hidden information of interactions among residues, which offers a novel approach that could comprehensively represent protein sequences. The proposed method was evaluated on several benchmarks using various performance measures, and the results indicate that the KC-DTA has a high performance on drug-target affinity prediction.Our approach, called KC-DTA, involves the mapping of the protein sequence into two matrices using k-mers segmentation analysis and Cartesian product calculation. In the prediction of drug-target binding affinity, it is common to consider the interaction between small molecules and proteins. This interaction typically occurs in a specific region of the protein, which is represented as a continuous segment in the protein’s amino acid sequence. To acquire protein information more accurately, we employ the k-mers method, which captures local segments of the protein, aiding in a deeper understanding of its structure and function. Furthermore, we also utilize the Cartesian product method to account for interactions between amino acids that are farther apart in the protein sequence. Through the Cartesian product, we can obtain combinations of all amino acids in the protein, facilitating the identification of effective positions within the protein and thus a more comprehensive understanding of its function and interactions. Combining the k-mers method with the Cartesian product method and considering local segments and all combinations of amino acids allows for a more comprehensive representation of protein sequence features. In addition, we represent the molecule as a graph. The resulting protein matrices and molecule graph are then fed into neural networks for feature extraction. The complete model architecture is illustrated in P = {A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, X, Y}, which each element of P represents single-letter abbreviation of the residue (amino acid). Next, the combinations of all three-residue are listed to form a combination set S is “ACDAG”, then the sequence is first applied by the k-mers (k = 3) segmentation with a step of 1,which results in a set S. Finally, the occurrences of the segmentations in L is the length of P and the value of The protein sequence is represented by two ways and two corresponding matrices are generated. The first way to represent sequence is to count the occurrences of k-mers segmentation for the target protein. For instance, assuming the residue symbol set of protein is P = {A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, X, Y}. But when form the full combination set, it is different from the first way, which the set is established by listing all two-residue combinations and result in a set S is defined as S}, where a, b are residues belong to sequences S. More specifically, if the sequence of the target protein S is “ACDAG”, the Cartesian product operation on the sequence S could yield a set of The second way to represent protein sequence is to transform sequence into another matrix using Cartesian product calculation operation. In this approach, we employ the Cartesian product to process protein sequence, which all possible combinations of two residues in a protein sequence are represented, enabling a more accurate capture of the protein features. For example, similar to the first way, at first, a set of symbols that can represent all possible protein sequences is denoted as After applying the two aforementioned methods to process protein sequence, a 3D matrix and a 2D matrix are obtained. These matrices contain rich evolution information about target protein, enabling effective description of protein sequences and further utilization in predicting binding affinity. Importantly, the generation of these two matrices is straightforward and does not require assistance from other tools, making them adaptable to any protein with a sequence and highly efficient for large-scale virtual screening applications.To represent the features of small molecules, we utilize a graph-based approach. The SMILES of molecule is read by RDKit toolkit , and it Due to the two processing ways of the protein, there are two networks used for the protein feature extraction. Thus, the whole model involves three entrances and three corresponding networks, which two of them are used for protein feature mining and the other one is used for molecule feature mining. Then, the three obtained hidden features are concatenated to prediction the affinity. The whole model architecture is illustrated as Because of the difference of the two processing ways for protein sequence, there are two matrices generated with different shapes. The first processing is utilized with k-mers segmentation with a k of 3 and the shape of the generated matrix Since the molecule is represented by graph, a graph neural network (GNN) is utilized for its feature extraction. The model is utilized with five GNN layers and each layer is followed by a batch normalization layer. To find the best GNN model, we tried to establish the GNN model with three different types of GNN layers: GCN , GAT (graph attention network), and GIN (Graph Isomorphism Network), and the performance of different types of GNN layers was thoroughly discussed in the experimental section.The Graph Convolutional Network (GCN)  is a speW. For each input node GAT , short fAfterward, the output features And GIN , or GrapHere, Because different molecules have different number of atoms, the molecular graph could be constructed with different number of nodes. So, a global pooling layer is added to ensure that the hidden vectors extracted for different molecules could have the same dimension. Finally, the two hidden features of protein and the hidden feature of molecule are concatenated and put into a fully connected neural network to predict the affinity.Four benchmark datasets were utilized in this experiment, namely Davis , KIBA T, Metz M, and PDBDavis dataset: The Davis dataset comprises selectivity assays of kinase protein families and associated inhibitors, along with their respective dissociation constant  is widely used to evaluate the performance of regression models and could be used as an evaluation measure of prediction accuracy. The CI for a set of paired data is equal to the probability that two drug-target pairs with different label values are predicted in the correct order, and a higher CI value indicates a better predictive performance. The calculation of CI is defined as follows:N is a normalization constant representing the number of pairs in the correct order, MSE is the most popular metric for evaluating regression models and measures how close the fitted line is to the actual data points. MSE is defined as the mean of the sum of squares of the differences between the true value and the predicted value. So, a lower value of MSE indicates a better predictive performance. The MSE calculation is defined as:n is the number of samples, and MSE is the average of the sum of squares of the difference between the predicted value and the actual value. The smaller the MSE value, the higher the efficiency of the regression model. Root mean squared error (RMSE) is the square root of the root mean square error, which is used in the PDBBind dataset in this experiment.Another metric ment}rm2  has been studies .In order to obtain the best model for the drug-target affinity prediction, we employed nine combinations for the model construction. For protein, we utilized three approaches of model construction: a single 2D+CNN for the feature extraction on two-dimensional matrix (2D) derived from Cartesian product calculation operation, a single 3D+CNN for the feature extraction on three-dimensional matrix (3D) derived from k-mers analysis, and both 2D+CNN and 3D+CNN involved for the two matrices. For molecule, three types of GNNs are utilized, which involve GCN , GAT Ve, and GINFrom From For the Davis and KIBA datasets, we employ a five-fold cross-validation method applied in DeepDTA . This in KronRLS , SimBoos KronRLS , DeepCPI KronRLS , DeepDTA KronRLS , GANsDTA KronRLS , MPS2IT- KronRLS , WideDTA KronRLS  are invoThe same measure with MGraphDTA is utilized for this experiment, which the dataset is randomly divided into five parts with a ratio of 4:1 and the four parts are used as the training set and the last part is used as the test set. To ensure the robustness of the proposed method, we performed the random division three times using different seeds, resulting in three distinct experimental outcomes. The final experimental result was obtained by computing the average of these three outcomes. There are three performance metrics used by the Metz dataset, namely MSE, CI, In this experiment, three methods were included for the performance comparison, which are Affinity2Vec , MoleculKC-DTA processes protein sequences using k-mers and Cartesian product, avoiding truncation of protein sequences and preserving the integrity of important information. Additionally, KC-DTA does not rely on complex algorithmic tools, allowing it to quickly transform protein sequences. This simple, fast, and effective method can be easily applied to large batches of protein and small molecule activity predictions. From experimental results, KC-DTA performs better overall on the Davis, Metz, and KIBA datasets compared to other methods. On the PDBBind dataset, our approach slightly lags behind Affinity2Vec. The PDBBind dataset has only 2,188 training samples, and these limited data may not be sufficient for the model to fully fit, thus preventing it from realizing its full potential. In summary, KC-DTA excels on medium to large datasets and demonstrates relative stability on small datasets, making it a promising deep learning approach.In addition, our method has not yet been applied in practical cases, making it difficult to verify its performance on larger datasets or in scenarios with more severe situation. In the future, we will attempt experiments with a broader array of databases to ensure that the model can adapt to a wide variety of situations, thereby enhancing the robustness of KC-DTA.The accurate prediction of drug-target affinity is essential for the development of effective pharmaceuticals. While identifying whether a drug binds to its target is important, it is equally crucial to predict the precise value of the drug-target affinity. In this study, we propose a novel sequence-based method for predicting drug-target affinity using protein sequence and drug SMILES. By leveraging k-mers analysis operation and Cartesian product calculation methods, we convert protein sequences into two different matrices, which could better capture the contact among residues and evolutionary information in sequence. The proposed method is easily implementable and applicable to large-scale virtual screening tasks.


Protein-mediated interactions are the fundamental mechanism through which cells regulate health and disease. These interactions require physical contact between proteins and their respective targets of interest. These targets include not only other proteins but also nucleic acids and other important molecules as well. These proteins are often involved in multibody complexes that work dynamically to regulate cellular health and function. Various techniques have been adapted to study these important interactions, such as affinity-based assays, mass spectrometry, and fluorescent detection. The application of these techniques has led to a greater understanding of how protein interactions are responsible for both the instigation and resolution of acute inflammatory diseases. These pursuits aim to provide opportunities to target specific protein interactions to alleviate acute inflammation. The regulation of cellular processes by proteins forms the foundation of all cellular biology and pathobiology. Fixed or transient interactions of proteins with their respective targets of interest allow for broad, dynamic modifications of cell mechanisms for both health and disease . Targetshttps://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml). In addition, both the hydrophobicity and shared bonding of hydrogen account for other mechanisms through which proteins can interact to modify one another  present in carbohydrates allow for the potential of hydrogen bond crosslinking . Likewishttps://string-db.org/) allows for the analysis of both physical and functional interactions between multiple proteins of interest. Understanding the mechanisms of protein-mediated interactions, and the tools to assess them, have allowed for investigations into the relevance of these relationships during acute inflammation.The capture of protein-mediated interactions can be challenging due to their dynamic and transient nature. Typically, for a protein-mediated interaction to be considered meaningful, the interaction should be considered intentional  and with a specific biological purpose to induce a physiologic change . Some moAt the most basic level, protein-mediated interactions are the drivers of the cellular response to acute inflammatory challenges. Starting with either cytokines, damage-associated or pathogen-associated molecular patterns (DAMPs and PAMPs), interactions of these molecules with pattern recognition receptors (PRRs) start a signaling cascade. During an inflammatory challenge, this cascade is initiated and propagated by a series of protein-mediated processes that include protein-protein, protein-DNA/RNA, protein-carbohydrate, and protein-lipid interactions to drive the biological response . One exaThe protein-mediated signals that occur at the plasma membrane and beyond are typically the result of a series of assembling and dissembling multi-protein complexes that exert their effect on various parts of the cell. These multi-protein complexes aid in signal transduction through a variety of post-translational modifications to induce protein targets to carry out specific functions . Signal As mentioned previously, a majority of DNA does not code for a specific protein. Nevertheless, these non-coding regions have important regulatory functions in host defense . In one The crux of a host response to combat a severe inflammatory challenge is to ensure that the energy needs of the cells are met . Under rThe importance of understanding protein-mediated interactions cannot be understated, especially in the context of drug discovery to mitigate disease. Potentiating or inhibiting key interactions during a disease process provides the possibility to hasten or restore homeostasis. As mentioned previously, the foundation of most protein-mediated interactions involves a lock-and-key interaction. Interactions can be directly targeted through orthosteric mechanisms or via indirect targeting through allosteric mechanisms . Lu et aThe use of large-molecule biologics to regulate inflammation has been well-established for chronic inflammatory conditions. In these settings, the biologics act as scavengers of pro-inflammatory soluble molecules circulating in the blood or as receptor antagonists . Many ofNCT04311697) using synthetic vasoactive intestinal peptide (aviptadil) in the treatment of COVID-19 is currently awaiting results, overall demonstrating the feasibility and efficacy of these therapeutics in acute inflammatory diseases. Beyond this, the discovery of future small molecule compounds is likely to accelerate with the development of high-throughput compound screening libraries. For instance, utilizing such a library revealed that niclosamide, approved as an anti-helminthic drug, had broad anti-inflammatory properties via inflammasome inhibition that worked against coronavirus, as well as several different TLR ligands  or myocardial infarction (NCT05350969). While currently there are no miRNA therapeutics aimed at regulating acute, acquired inflammatory diseases, such as sepsis or COVID-19, the concept has gained some traction (NCT05216562). Though the therapeutic application of miRNAs to successfully treat acute inflammatory diseases remains to be seen, they nevertheless continue to be a topic of significant investment.Manipulation of protein production and signal transduction by miRNAs has been previously discussed. Given the wide impact miRNAs can have on proteins, it makes them an attractive potential therapeutic avenue for targeting cellular processes . Data thtraction . RegardiGreat strides have been made in recent decades that have begun to pull back the veil of complexity that is the host’s defense to acute inflammatory challenge. With the advent of more sophisticated techniques, coupled with enhanced computing tools and machine learning, we now know more about the interconnected and dynamic proteome involved in modulating a host’s response. This knowledge has been further expanded by understanding new facets of the role of the genome in producing non-coding RNA, which regulate protein structure and function, as well as the importance of multi-protein complexes forming the appropriate metabolic response to an inflammatory challenge. As these protein-mediated interactions are further elucidated, high-throughput screening techniques that repurpose old drugs, or allow for the creation of new ones, will hopefully allow for the correction of aberrant protein interactions that instigate and perpetuate acute inflammatory conditions. The development of such compounds will no doubt usher in a new era of therapeutics that will provide improved outcomes for those patients suffering from diseases associated with acute inflammation.


Cell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality.We develop a simulation-based Bayesian MCMC method employing an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: an activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components.https://github.com/Mathbiomed/SimMCMC.Our code is implemented in R and is freely available with a simple example data at  Advances in microscopy allow us to observe the dynamics of gene regulatory networks (GRNs) in unprecedented detail. Novel statistical techniques have helped interpret the resulting wealth of data. However, even the best experimental methods can provide observations of only a fraction of the components constituting a GRN, and thus offer only partial information about the dynamics of gene circuits. Statistical methods thus need to take into account the effect of unobserved processes to correctly interpret the data, and accurately characterize genetic circuits and their dynamics.x and y, where protein X regulates the expression of gene y, most present methods can be applied only if the counts of both proteins X and Y are known. Often it is impossible to observe the products of both genes concurrently. Is it possible to characterize the dynamics of X, if only Y is observed?Recently, inference methods have been proposed based on the assumption that the unobserved processes are sequential, and thus can be modeled by introducing a delay . Thus, iCurrently available Bayesian Markov chain Monte Carlo (MCMC) methods for inference of non-Markovian systems are not well suited to address this question. While these methods are applicable even to systems with high intrinsic noise (i.e. low copy numbers of molecules)  where unobserved AraC protein triggers the expression of yellow fluorescent protein (YFP). When the dilution rate and the time delay for the synthesis of the observed protein YFP are separately measured, we are able to infer the time delay for target regulation by the unobserved AraC protein . This finding can play a critical role in synthetic circuit design because the AraC protein, whose kinetics has yet to be fully characterized, is a widely used transcriptional activator in synthetic biology. Our study also illustrates how information from unobserved proteins can be inferred from the dynamics of observed proteins in GRNs. Our approach is scalable and provides a tool for characterizing non-Markovian systems from partial observations.Here, we present a simulation-based Bayesian method for the inference of kinetic and delay parameters of a GRN when only the products of some of the genes in the network are observed. The approach is applicable generally even if only the most downstream genes, i.e. the final outputs, of the network are observed. We illustrate the method using a two-step activation model, where an initial signal activates gene In this section, we derive a likelihood function of a general biochemical reaction network with delays. The detailed form of the likelihood function for the two-step activation model is provided in the u species v reactions We first derive a likelihood function to construct a Bayesian inference method for estimating kinetic and delay parameters   left   right. WTo estimate the remaining parameters we applied our MCMC algorithm to the measurements of the observed fluorescent protein and obtained estimates of the posterior distributions for the parameters riment 2 . This diriment 2 . On the 0.34 min .The estimated time delay for the transcriptional regulation is the sum of delays corresponding to AraC synthesis, diffusion, and binding-site search. Interestingly, the binding-site search time of transcriptional factors in prokaryotic cells is usually less than a few minutes even for chromosomal genes whose copy number is just one or two  approach (Thus, the key step of the present method is the use of a stochastic simulation algorithm. Since we use the rocesses , indepenrocesses , which irocesses . In contWe assumed that all cells in a population are identical, and thus share the same parameters, i.e. we ignored cell-to-cell variability. Even isogenic cell populations can show significant cell-to-cell variability , and hetOur approach is scalable. While we demonstrated our inference framework using only the two-step activation model, the simulation-based MCMC can be tailored to more complex GRNs using the likelihood function we derived for a general biochemical reaction network . The metA simulation-based MCMC method can be developed for other dynamical models as long as a likelihood function is available. While we used a continuous-time Markov Chain, which efficiently explains a biochemical reaction network with a low copy number of molecules, one can also use a stochastic differential equation (btad670_Supplementary_DataClick here for additional data file.


Protein Recognition and Associated Diseases” addresses the recent advances in computational methodologies for the analysis and identification of important residues for binding, scoring, and ranking of structural models of protein-protein complexes, protein-protein interaction networks, and their applications in life sciences and human health.Protein-protein interactions are essential for many biological functions in all living organisms including cell signaling, molecular switching, transporters, receptors, and immunity. For the past few decades, tremendous advancements have been made in order to understand the recognition mechanism of protein-protein complex formation, reconstruct protein-protein interaction networks of an entire organism, and/or complete biochemical pathways. These efforts are mainly focussed on the identification of interacting proteins, prediction of binding site residues at their interface, evolutionary conservation of protein-protein complexes, prediction of protein-protein complex structures by docking, predicting the binding affinity of protein-protein complexes, and assessing the mutational effects on strength of binding and diseases . RecentlBrysbaert and Lensink analyzes the performance of several centrality measures for identifying major interacting residues involved in protein-protein binding using binding affinity data of interface mutations. Johansson-Åkhe et al. propose a machine learning-based method for scoring and ranking peptide-protein complexes. It encodes the structure of the complex as a graph with evolutionary and sequence features as nodes and physical pairwise interactions as edges. Su et al. integrate protein-protein interaction networks and gene expression profiles for detecting pancreatic adenocarcinoma candidate genes. Karan et al. report the development of four genomic information-based prediction methods, namely, 1) interolog, 2) domain, 3) gene ontology, and 4) phylogenetic for identifying the interaction between Oryza sativa and Magnaporthe grisea in a whole-genome scale.The opening article by In essence, this Research Topic covers the exciting developments in the area of protein-protein interactions both at fundamental and application levels. It will be a valuable resource for computational biologists, biochemists, biophysicists, bioinformaticians, and researchers working in the field of protein-protein interactions, and for those working on the biological role of protein-protein interaction networks and their relation to disease.We would like to thank all the authors for their outstanding contributions. The guest editors thank the Editorial Assistant Dr. Sara Gomez Cabellos, Commissioning Specialist, and Dr. Rahila Esposito, Commissioning Manager for their help and support in successfully completing the Research Topic.