Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins , 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals. Computational approaches to drug resistance prediction are of great interest for the development of new drugs in order to overcome mutation-induced drug resistance. Here, the authors develop a state-of-the-art database, MdrDB, which integrates data from seven publicly available datasets, expands the existing drug resistance data, and enhances the performance of classical machine learning models for drug resistance prediction. In some cases, it can result in significant perturbations or even complete abolishment of protein function, potentially leading to diseases or cancer10. The evolutionary pressure imposed by small molecule drugs on many quickly evolving systems, including cancer cells, viruses, and bacteria, can lead to the rapid development of resistance14. While novel and cheap high-throughput sequencing technologies have made it possible to identify mutations in large populations16, the significance and characteristics of any novel polymorphisms currently require time-consuming and expensive experiments to determine17. Protein-ligand binding affinity data are of great value for understanding the impact of polymorphisms on disease and identifying mutations that lead to drug resistance19. Convenient and broad access to such data for wild-type and mutant proteins would aid our understanding of the mechanisms of mutation-induced drug resistance, increase the accuracy of extrapolations to novel mutations and systems, enable more effective computational approaches for drug resistance prediction, and facilitate the development of combination therapies and the discovery of novel chemicals.Structural mutation of proteins can directly affect their folding and stability17 was the first database to provide experimental data on changes in protein-ligand affinities upon mutation, along with three-dimensional structures, while tyrosine kinase inhibitor (TKI)18 contains reliable inhibitor ΔpIC50 data for 144 clinically identified mutants of the human kinase Abl. Together, these two datasets are the current gold standards, both providing well-curated data and three-dimensional structural information. Other resources include Auto In Silico Macromolecular Mutation Scanning (AIMMS)20, RET21 and kinase mutations and drug response (KinaseMD)22, which provide kinase inhibitor resistance information. However, while the value of these datasets is significant, there remain a number of deficiencies. Platinum and TKI are restricted to known cocrystal structures and thus contain relatively few samples , which can limit their use with machine learning models. For AIMMS, RET, and KinaseMD, structural information is not provided. In addition, while all of the aforementioned databases consider single-point and multi-site substitution mutations, they do not include more varied and complex mutation types—such as deletions, insertions, and insertion-deletions (indel)—which play an important role in certain disease progressions.Fortunately, a number of databases on the effects of mutation on protein-ligand binding affinity have been compiled and released in recent years. In particular, Platinum23, and Cancer Dependency Map (DepMap)24. Further supplemented with data from supporting databases such as RCSB Protein Data Bank (PDB), UniProtKB and PubChem, and 3D structures computed with PyMOL and AlphaFold 2.025, MdrDB brings together 3D structures of wild-type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features calculated from complex structures. MdrDB contains 100,537 samples, generated from 240 proteins , 2503 mutations, and 440 drugs. A total of 95,971 samples are based on available PDB structures, and 4566 samples are based on AlphaFold 2.0 predicted structures.To tackle these challenges, we have created MdrDB, an all-encompassing database that significantly expands the amount of information on drug resistance. MdrDB combines the five databases mentioned above and achieves considerable augments by incorporating large-scale drug response data, and somatic mutation information from various cancer cell lines, as reported in the Genomics of Drug Sensitivity in Cancer (GDSC) datasetMdrDB offers several key advantages over existing publicly available protein mutation databases. Firstly, MdrDB offers massive and diverse data. With over 100,000 samples, MdrDB integrates information from multiple sources, includes various mutation types beyond single site-point mutation and covers mutations across a broad range of protein families, which is the largest mutation-induced drug resistance database to our knowledge. Secondly, MdrDB provides 3D structural information on all wild-type and mutant proteins along with 146 associated biochemical features, which are useful and important in accurate drug resistance modeling. Furthermore, we evaluated the classical drug resistance prediction machine learning models’ performance on three standard benchmarking scenarios. By using MdrDB as a training set, nearly all models gain significant performance improvement. In summary, MdrDB has the size, breadth, and complexity to offer new resources for protein mutation studies and drug resistance modeling.At the time of writing, MdrDB contains 100,537 samples, generated from 240 proteins , 2503 mutations, and 440 drugs. In all, 95,971 of the total samples are based on available PDB structures, with the remaining 4566 samples predicted with AlphaFold 2.0. In addition to single-point substitution mutations and multiple-point substitution mutations, MdrDB also contains complex mutations including deletion, insertion, and indel (insertion-deletion) mutations, as well as multiple-site mutations containing a number of the aforementioned mutations. Table G, is shown in Fig. 18, where samples with a larger than 10-fold drop in binding affinity, corresponding to ΔΔG >1.36 kcal mol−1, are considered to be resistant. Based on this standard, 8197 (8.2%) samples in MdrDB are mutation resistant.The distribution of mutation-induced ligand binding affinity changes, measured by ΔΔ26. Leucine (L), alanine (A), serine (S) and cystenine (C) are the most frequently mutated amino acids. Similar conclusions hold for the MdrDB_CoreSet . For the amino acids which deviate the most from this expected spectrum, a higher ratio proportion of lysine (K), glutamine (Q), cysteine (C), and tryptophan (W) are mutated to A, which may come from the loss of function mutation experiments, such as alanine scanning mutagenesis30. We carried out the same analyses on susceptible/resistant samples , where serine (S), lysine (K), and tyrosine (Y) showed the largest differences. For the frequency of each amino acid after mutation in the susceptible /resistant samples, although the correlations are similar, their distributions vary greatly.The mutation frequencies of different amino acids at the mutation sites are highly correlated with the frequencies calculated based on codon frequencies  with 484 training samples and then tested on the TKI dataset—achieved low performance in terms of correlation and classification ability. To alleviate the overfitting problem, our previous work38 incorporated additional physics-based structural features and protein family information as inputs to learn more comprehensive knowledge from limited training data. The empirical results show that the method we previously proposed is slightly superior to MD simulations with the Amber force fields. In this section, we investigated three scenarios to test whether using the MdrDB_CoreSet as the training dataset  can improve the model generalization performance in predicting mutation-induced drug resistance in the TKI dataset.Rapid and accurate computational methods could impact clinical decision making by providing oncologists with an initial indication of whether an observed protein mutation may lead to drug resistance to certain inhibitors. Molecular dynamics (MD)-based free-energy calculations and physics- and knowledge-based potential scoring functions  are two common types of computational methods for estimating affinity changes in proteins with mutations38. The second scenario was to train the machine learning methods on the Platinum dataset, and test them on the TKI dataset. The third scenario was to train the machine learning methods on a subset of MdrDB_CoreSet, removing samples derived from the TKI dataset and selecting samples corresponding to single substitutions, and then test them on the TKI dataset. Since the TKI dataset contains only the single substitution type, we select samples belonging to a single substitution in MdrDB_Coreset. Compared with Scenario 1, the latter two scenarios evaluate whether adding a small amount of tyrosine kinase information or increasing samples and protein family information in the training dataset can improve the capability of the model in predicting affinity changes in Abl kinase mutants. Detailed information on the training datasets for the three scenarios is given in Supplementary Table We conducted experiments on the TKI dataset under three scenarios. The first scenario was to train the machine learning methods on the subset of the Platinum dataset, which had no tyrosine kinase samples, and then test them on the TKI dataset. This setting is consistent with the setting in work39, random forest (RandomForest)40, and extremely randomized regression trees (ExtraTrees)41. The second family is linear-based methods containing support vector regression (SVR)42, elastic net linear regression (Elastic Net)43, and lasso regression (Lasso)44. The third family of baselines is ensemble-based methods including bagging regressor (Bagging)45, AdaBoost46, and gradient boosting (GradienBoost)47. The fourth family is neural network-based methods such as multi-layer perceptron48.Four families of methods were used to evaluate the drug resistance prediction performance under three scenarios. The first family is tree-based methods, including decision tree (DecisionTree)19. Consistent with the previous work38, resistant mutations are defined as the affinity changes for mutants by least 10-fold, i.e., ΔΔGexp >1.36 kcal mol−1.Root mean square error (RMSE), Pearson correlation coefficient (Pears), and the area under the precision-recall curve (AUPRC) were used to evaluate the model performance under three training scenarios−1), strong correlation (Pearson = 0.607), and good classification performance (AUPRC = 0.538). This result outperformed MD simulations with the Amber force fields  reported in the work19 by a considerable margin, demonstrating that MdrDB_CoreSet could improve the generalization of the machine learning method in predicting affinity changes in Abl kinase mutants. Figure G values of each machine learning method obtained at a certain time by training on the subset of MdrDB_CoreSet and testing on the TKI dataset. The scatter plots of the experimental versus calculated ΔΔG values in the first and second scenarios are shown in Supplementary Figs. We train the machine learning methods on different training datasets , Platinum, and MdrDB_Coreset (single substitution)) and report the test performance averaged over five repetitions for each method on the TKI dataset in Fig. In addition, we also provide a comprehensive evaluation of 10 common machine learning models in several different scenarios  and experimental cocrystal structures of protein-drug complexes from the RCSB PDB. It contains more than 1000 manually curated mutations20.AIMMS: a web server for predicting site mutation-induced drug resistance, and contains a dataset of 17 protein-ligand complexes involving 311 ΔΔG values and mutations18.TKI: a database of TKIs resistances in ABL tyrosine kinase site mutations. A total of 144 ΔΔG values are included, along with ABL-TKI complex structures from PDB21.RET is a dataset specifically related to drug resistances between three drugs  and the RET kinase mutants. 56 IC50 measurements and protein-drug complex structures were reported in the research49 and GDSC23 databases, and provides various annotations of drug responses on kinase mutants. In all, 79 ΔΔG values were collected from this database22.KinaseMD: focuses on kinase mutations, and integrates information from the Cancer Cell Line Encyclopedia (CCLE)GDSC: the largest public resource for information on drug sensitivity in cancer cells. The database contains 4.4 million drug sensitivity (IC50) values across 518 drugs and 1000 cancer cell lines. It also records information related to basal expression, mutation, copy number variation, and gene methylation in the cell lines.50 and CCLE projects51.DepMap: similar to GDSC, and contains drug sensitivity data on cell lines from the AchillesMdrDB contains data from seven publicly available datasets: PlatinumFor Platinum, AIMMS, TKI, RET and KinaseMD, mutation information and mutation-induced binding affinity changes are directly obtainable from the datasets. However, for GDSC and DepMap, extra steps are required to obtain the mutations and ΔΔG values (see next section).Mutation and ΔΔG values were obtained from these two datasets via a process similar to that used by KinaseMD: (i) mutation information acquisition; (ii) drug-cell line response calculation; and (iii) ΔΔG calculation.Single substitution mutation: replacement of one amino acid in a protein with a different amino acid.Multiple substitution mutation: more than one amino acid substitution in a protein.Deletion mutation: removal of one amino acid or an amino acid sequence in a protein.Insertion mutation: addition of one amino acid or an amino acid sequence in a protein.Indel mutation: a deletion mutation followed by an insertion mutation, i.e., replacement of an amino acid sequence in a protein with another amino acid sequence.Complex mutation: combinations of the above five amino acid mutation types.Cell lines were first grouped into wild-type and mutated samples for a specific protein, and mutation information for the protein was gathered. Specifically, for a particular protein, cell lines that did not have mutations on them were considered to be control (wild-type samples), while cell lines with mutations were considered to be mutant. Mutations were then filtered to retain only six types of mutation in terms of amino acid changes in protein sequences:22. This average value was taken as the mutant protein-drug affinity value IC50(mut). A corresponding wild-type sample was similarly assigned and IC50s again averaged over cell lines, and this average value was taken as the wild-type protein-drug affinity IC50(wt).Then, we filtered the drugs with known protein targets and queried drug sensitivity  data on all cell lines. For drugs with multiple known protein targets, each protein was considered individually. If, in the cell line, only one of these proteins was mutated, the cell line was kept. Otherwise, the cell line was discarded. After this, for each  mutant sample, the mutation string was generated by merging all mutations of the mutant protein to obtain a  sample. Then IC50 values were averaged over all cell lines for the  sample following the sensitivity data processing in KinaseMD37:Finally, these affinity pairs were used to calculate ΔΔG according to the formula52 with protein names. The consolidated data was then divided into separate tables, with samples of the same mutation type kept together. For samples from Platinum and TKI, wild-type and mutant protein-drug complex structures were also obtained from the original datasets. For protein annotations, we used the Interpro API53 to query the protein family, homologous superfamily and domain information. For drug annotations, we used the PubChem PUG REST API54 to query the CID, Depositor-Supplied Synonyms, FDA mechanism, MeSH and Drug Classes information.From all seven datasets, five basic pieces of information were collected and consolidated: protein name, UniProt ID, mutation string, drug name, and ΔΔG value. UniProt IDs were identified by querying UniProtKBWith the exception of samples from Platinum and TKI, which already directly include 3D structure information, we prepared 3D structures of wild-type proteins, mutant proteins and drug binding poses for all samples from the other datasets.55. The .pdb or .cif for 3D structures and .fasta for sequences were downloaded from RCSB PDB. Then, all water, solvent, and ions were removed from the PDB files. Next, the protein chains and ligands were split into separate files. Each chain was annotated and only the chains corresponding to the UniProt ID were kept. If multiple chains were found to exist for protein, the longest one was kept. Meanwhile, the largest ligand was kept as the docking box generation reference. Finally, each mutation for a protein was checked against all available PDBs. If the mutation sites could be found in the PDB, the mutation and drug would be assigned to the PDB.For the preparation of wild-type protein structure, first for each protein (UniProt ID), all associated PDBs were identified with the RCSB REST API56 or from wild-type sequences using AlphaFold 2.025. Specifically, the Mutagenesis Wizard module of pymol-open-source v2.5.0 was used to generate a mutation by replacing a residue with a new amino acid type, sample several rotamers from the rotamer library and generate several non-clashing conformations. Then, the most likely rotamer was chosen as the mutated residue. For AlphaFold 2.0, we used the protein amino acid sequence as the input to predict the structures. A length threshold of 2000 was set for computing resource considerations. For Multiple Sequence Alignment searching, “reduced_db” was used. For inference, the “monomer_ptm” models were used. Five models were generated and the one with the highest averaged pLDDT (predicted lDDT-Cα) value was chosen as the predicted structure for further procedures. Several rules were followed to decide which tool was used for mutant structure generation. For proteins with known PDBs containing the mutation sites:For single substitution and multiple substitution mutations, PyMOL was used for mutant protein generation.For deletion, insertion, indel and complex mutations, AlphaFold 2.0 was used for mutant protein structure prediction. For fair comparison and feature calculation, post-processing was carried out to keep the residue numbers the same except at the mutated sites.For the mutant protein structure generation, we generated mutant structures from wild-type structures using PyMOLFor proteins with no known PDBs containing the mutation sites, AlphaFold 2.0 was used for both wild-type protein and mutant protein structure prediction. Structures with an average pLDDT larger than 70 for the whole structure were kept, which was a confidence threshold for the predicted structures in AlphaFold 2.0. In addition, if a mutated site was located in a region that was poorly predicted (pLDDT < 50), the sample was also discarded. After structure generation, the mutant protein was aligned with the wild-type protein. This alignment was carried out by only taking the backbone atoms of both wild-type and mutant residues whose pLDDTs were larger than 70 into consideration.54 . Ions and salts were then removed from the SMILES, the structures neutralized, and the resulting SMILES strings rewritten in canonical format. 3D structures were first generated using Open Babel 3.1.157, using the “–gen3D” flag, and non-polar hydrogens were added to the generated structures with the “–addpolarH” flag. After structure generation, molecular docking was carried out with smina58, using default docking parameters:If the wild-type PDB contained a known in-pocket ligand, then “–autobox_ligand” was selected.If no ligand was present, the whole protein was used to generate the docking box.For drug structure generation, SMILES strings for all drugs were first obtained using the PubChem PUG REST APIAfter docking, the conformation with the best smina score was kept.19, and we follow the procedures in their original paper for their calculation:Eighteen ligand properties such as logP, molecular weight, number of hydrogen acceptors and donors were calculated using RDKit.Twelve features describing the changes for the mutated amino acids were calculated (again with RDKit), such as hydrophobicity, number of heavy atoms, and the change in side-chain volume.Twenty-one features describing the mutation environment were calculated with Biopython, including the distribution of ligand and protein atoms around the mutation site and the number of residues in different property groups.59: hydrogen bonds, hydrophobic contacts, salt bridges, π-stacking, cation-π interactions, and halogen bonds.Six features related to the protein-ligand interactions were calculated using the Protein-Ligand Interaction Profiler61.The Vina binding score as well as 58 scoring function terms were calculated using Delta-Vina XGBoost61.Thirty pharmacophore-based solvent accessible surface area features are calculated using Delta-Vina XGBoostBased on the structures of the wild-type and mutant protein-ligand complexes, we calculated a total of 146 biochemical features relevant to machine learning prediction of mutation-induced affinity changes. These features were first used in the work of Aldeghi et al.x of all other features given in MdrDB correspond to the difference between the mutant (xmt) and wild-type values (xwt), i.e., x = xmt − xwt.With the exception of the ligand property features, the numerical values caddy2 and Bootstrap v5.0. All processed data, structure files, biochemical features and annotations were stored in Tencent Cloud Object Storage. Interactive charts were implemented using Apache ECharts. A full tutorial for MdrDB is available at https://quantum.tencent.com/mdrdb/tutorial. A user-friendly website provides access to the curated data and structure information on wild-type and mutant complexes, and provides functions for browsing, searching, displaying and downloading the data. For detailed information on the constructed website, please see Supplementary Note The web interface of MdrDB was implemented using Peer Review FileSupplementary Information


Type 2 diabetes (T2D) develops from insulin resistance (IR) and the dysfunction of pancreatic beta cells. The AKT2 protein is very important for the protein signaling pathway, and the non-synonymous SNP (nsSNPs) in AKT2 gene may be associated with T2D. nsSNPs can result in alterations in protein stability, enzymatic activity, or binding specificity. The objective of this study was to investigate the effect of nsSNPs on the AKT2 protein structure and function that may result in the induction of IR and T2D. The study identified 20 variants that were considered to be the most deleterious based on a range of analytical tools included . Two mutations, p.A179T and p.L183Q, were selected for further investigation based on their location within the protein as determined by PyMol. The results indicated that mutations, p.A179T and p.L183Q alter the protein stability and functional characteristics, which could potentially affect its function. In order to conduct a more in-depth analysis of these effects, a molecular dynamics simulation was performed for wildtype AKT2 and the two mutants (p.A179T and p.L183Q). The simulation evaluated various parameters, including temperature, pressure, density, RMSD, RMSF, SASA, and Region, over a period of 100 ps. According to the simulation results, the wildtype AKT2 protein demonstrated higher stability in comparison to the mutant variants. The mutations p.A179T and p.L183Q were found to cause a reduction in both protein stability and functionality. These findings underscore the significance of the effects of nsSNPs (mutations p.A179T and p.L183Q) on the structure and function of AKT2 that may lead to IR and T2D. Nevertheless, they require further verifications in future protein functional, protein–protein interaction, and large-scale case–control studies. When verified, these results will help in the identification and stratification of individuals who are at risk of IR and T2D for the purpose of prevention and treatment.  Diabetes mellitus (DM) is a metabolic disease characterized by elevated blood sugar. The general types of DM include type 1 DM, which is caused by the absence of insulin due to pancreatic beta cell destruction mediated by autoimmune mechanisms . The thiThe serine/threonine kinase AKT2 is an effector protein in the insulin-signaling pathway and is connected to insulin metabolic actions . It is ePI3K/AKT signaling is crucial for regulating insulin responsiveness and other physiological and pathological processes, including growth, glucose homeostasis, lipid metabolism, protein synthesis, cell proliferation, survival, and angiogenesis ,19,20,21IR and abnormalities in lipid metabolism have been associated with the dysfunction of AKT2 and other elements of the insulin signaling pathway ,24. Genehttp://www.ncbi.nlm.nih.gov (accessed on 23 January 2023). The single nucleotide polymorphisms (SNPs) databases for the AKT2 (GLU17LYS) gene can be found in the NCBI’s SNP database (SNP), which is accessible at http://www.ncbi.nlm.nih.gov/snp/ (accessed on 23 January 2023). Similar data were acquired from the Online Mendelian Inheritance in Man (OMIM) database (http://www.omim.org (accessed on 23 January 2023)) regarding the AKT2 gene and protein.The FASTA-formatted nucleotide sequence (Accession number: NG 012038.2) and amino acid sequence (NP 001229956.1) of the AKT2 protein were obtained from the National Center for Biotechnology Information (NCBI) database, which can be accessed at The assessment of potential effects of SNPs involved the utilization of several online tools and servers, namely the Sorting Intolerant From Tolerant (SIFT) Sequence, Polymorphism Phenotyping (poly-Phen2), Single Amino Acid Predication (SNAP2), and Protein Analysis Through Evolutionary Relationships (PANTHER). These tools and servers were employed to evaluate the impact of SNPs. Four servers were utilized to classify nsSNPs and assess their confidence ratings based on multiple inputs. The impact on protein function, specifically for non-synonymous single amino acid substitutions, can be determined by analyzing the predictions provided by SIFT, PolyPhen2, SNAP2, and PANTHER. These predictions classify the impact as either tolerable or damaging. The SIFT algorithm employs a tolerance index score to evaluate SNPs, whereby variations with scores below 0.05 are deemed deleterious. In contrast, the PolyPhen algorithm assigns a numerical score ranging from 0 to 1, where a score of 0 indicates a neutral effect and a score of 1 indicates the most severe negative effect. Another tool utilized in this study was SNAP2, which facilitates the comparison of genomes and provides predictions regarding functional consequences at the amino acid level. The SNAP2 algorithm utilizes a protein sequence and a set of Single Amino Acid Variants (SAVs) as input in order to forecast the impact of each substitution on the molecular function of the protein. The prediction scores span from a value of −100, indicating complete neutrality, to +100, indicating a significant effect. The final tool, known as PANTHER, employs a scoring system that spans from −1 to 1 in order to assess the projected influence of the input single nucleotide polymorphisms (SNPs) on protein functionality. The functionality of a single nucleotide polymorphism (SNP) can be deduced by evaluating its score. A positive score indicates that the SNP is functional and has an effect on protein function, while a negative score suggests that the SNP is non-functional. Greater absolute values are indicative of a more pronounced predicted impact on protein function. The single nucleotide polymorphisms (SNPs) of the AKT2 gene, which were found to be common among the fourth group of servers, were subsequently subjected to computational analysis.http://snps.biofold.org/phd-snp/phd-snp.html (accessed on 23 January 2023)) and SNPs&GO (http://snps-and-go.biocomp.unibo.it/snps-and-go/ (accessed on 23 January 2023)) were used for determining association of filtered SNPs with disease. The PhD-SNP online bioinformatics tool predicts the association between SNPs and diseases using machine learning methods. It assigns SNPs a score between 0 and 9 based on the probability of them resulting in a disease, classifying them as either disease-associated or neutral. The proportion of PhD accuracy SNP is 78% [Predictor of human deleterious single nucleotide polymorphisms (PhD-SNP) (P is 78% . SNPs&GOP is 78% . The Unihttp://folding.biofold.org/i-mutant/i-mutant2.0.html (accessed on 23 January 2023)), was utilized to forecast the effect of SNPs on the alteration of a protein’s stable state. It has been reported to possess an accuracy of up to 77% [https://mupro.proteomics.ics.uci.edu/ (accessed on 23 January 2023)) is a set of machine learning algorithms that detect alterations in protein states and stability due to amino acid mutations [Single nucleotide polymorphisms (SNPs) can modulate protein strength by either reducing or enhancing protein stability. In order to predict these effects with maximum confidence, a pair of tools was used. The tool known as I-Mutant, accessible at (p to 77% . The I-Mutations . The inphttp://mutpred.mutdb.org/#qform (accessed on 23 January 2023)) is a valuable resource for forecasting the prospective influence of missense mutations on the functionality of proteins. The Mut-pred tool utilizes a comprehensive set of fifty distinct protein properties in order to determine the impact of substitutions. The objective is to forecast the probability of a particular missense mutation being either deleterious or benign. The requisite data for Mut-pred comprises the amino acid sequence of the protein that encompasses the mutation, in conjunction with the identity and position of the mutated residue. The resultant output furnishes an anticipated pathogenicity score, along with particulars regarding the precise characteristics of the protein function that are projected to be impacted by the mutation. A MutPred2 score exceeding 0.5 indicates the presence of a pathogenic mutation.The Mut-pred web server (http://ConSurf.tau.ac.il/ (accessed on 23 January 2023). This tool enables the high-throughput prediction of functional characteristics pertaining to specific protein regions. A conservation analysis value is assigned to each protein residue on a scale ranging from 1 to 9. Residues with scores of 1–3 indicate variabilities, scores of 4–6 indicate average conservation, and scores of 7–9 indicate highly conserved regions. In order to utilize the application, it is necessary to input the protein sequence in FASTA format.The examination of the conservation of the AKT2 protein was conducted utilizing the web-based programme ConSurf, accessible at The software PyMol Version 2.0. was utilized to locate the binding site where alterations in amino acid composition took place. The utilization of pyMOL entails the examination of residues located in the active site. The aforementioned software is a tool that is open-source in nature and is utilized for the purpose of molecular visualization. It has the ability to produce three-dimensional images of small molecules with a superior level of quality. Thus, two potential mutants  were selected from the 20 nsSNPs deemed to be the most deleterious mutants in order to conduct further analysis through molecular dynamics (MD) simulation. GROMACS version 2020.6 was used on a Google Collaboration pro notebook with high RAM and python 3.8 to run molecular dynamics (MD) calculations on both wildtype and mutant structures to look into the structural changes over time. For the first computation, the GROMACS-OPLS-AA force field was used. The systems were submerged in a cubic box filled with water molecules that had marginal radii of 1 nm (Å10), and the simulation box was neutralized by introducing 10 sodium Na+ ions using the GROMACS genion tool. The energy minimization was performed using the steepest descent algorithm with an energy step size of 0.01 and a maximum of 50000 iterations. To maintain a stable system, the Parrinello–Rahman (pcouple) pressure of 1 bar and a Berendsen temperature (tcouple) of 300 K were used. For temperature and pressure, the coupling constant was changed to 0.1 and 2.0 ps, respectively.−5. The system was equilibrated for 100 ps each in NPT  and NVT  ensembles with the Berendsen temperature (tcouple) at 300 K and the Parrinello–Rahman (pcouple) pressure at 1 bar with coupling constants set to 0.1 ps and 2.0 ps for temperature and pressure, respectively. Subsequently, the native and mutant structures were subjected to 10 ns of molecular dynamics simulations, with trajectories recorded every 1 ps. The comparative analysis of structural deviations between the wildtype and mutant structures was conducted using various tools, such as g_rms, g_rmsf, g_sasa, g_Rg, and g_density, to analyze RMSD (root mean square deviation), RMSF (root mean square fluctuation), SASA (solvent accessible surface area), Rg (radius of gyration), pressure, temperature, and density plots. The application XMGRACE was used to create each plot.The electrostatic interactions in the system were computed using the Partial Mesh Ewald (PME) algorithm. The short-range cut-off for both Van der Waals (rvdw) and electrostatic (rcoulomb) interactions was set at 1.0 nm. The neighbor list (nstlist) was updated every 10 ps. All bond constraints, including heavy atom-H bonds, were maintained using the LINCS algorithm with a time step of 0.002 ps. The isothermal compressibility was set to 4.5 × 10The present study examined the association of nsSNPs in the AKT2 gene and IR and T2D. Understanding the effects of on protein function is crucial for unraveling disease genetics and finding potential therapies. Computational methods utilize evolutionary conservation, protein structure, and function to predict nsSNP impacts on stability and structure. Investigation of the nsSNP AKT2’s effects offers insights into its role in IR and T2D development, aiding in understanding of the underlying disease mechanisms and identifying therapeutic targets. In addition, predicting nsSNP effects in the AKT2 gene can pinpoint high-risk individuals for tailored interventions and advance comprehension of genetic variation role in IR and T2D. Deciphering nsSNP impact on AKT2 could lead to personalized treatments targeting genetic variants, improving IR and glucose regulation for those at T2D risk. We present our results on AKT2 nsSNPs and their relevance to IR and T2D susceptibility, aiming to contribute to disease understanding and therapeutic development. A summary of the results of the four different web servers, SIFT, PolyPhen2, SNAP2 and PANTHER, regarding the effects of different SNPs on the AKT2 protein are shown in The aforementioned table presents data pertaining to the SIFT score predictions for individual amino acid substitutions, which are utilized in the assessment of the effect on protein functionality. The data presented in the Nonetheless, certain substitutions are anticipated to be innocuous or slightly harmful, exhibiting elevated SIFT scores. In general, the SIFT score presents a valuable tool for forecasting the impact of SNPs on protein functionality. According to To predict the SNPs’ nature with high confidence, SIFT, PolyPhen2, SNAP2, and PANTHER servers were employed concurrently. SIFT and PolyPhen2 received 533 missense nsSNPs in total. After consulting the databases it is integrated with, it offered each reported SNP an index score. A total of 142 (27%) of the nsSNPs were indicated by SIFT indexing to be harmful with a score of ≤0.05. Moreover, 99 out of 533 nsSNPs were reported to be highly harmful with a confidence level of 0.00. A total of 99 of the nsSNPs were classified as “probably damaging” by the PolyPhen indexing, which is based on structural data and multiple sequence alignment, and 43 as “possibly damaging”. As a result, 99 (18.6%) of nsSNPs had negative PolyPhen predictions. Combining data from SIFT and PolyPhen allowed the removal of 142 nsSNPs in order to produce the most accurate results. After that, SNAP2 and PANTHER servers received these 142 nsSNPs for additional verification. The 99 nsSNPs were then sent to the SNAP2 server to determine their effect. The findings showed that 75 nsSNPs were found to affect the protein functionally. A summary of the results related the nsSNP functional effects is shown in p ≥ 0.80 was utilized for PhD-SNP scores, resulting in a total of 20 nsSNPs. We employed SNP&GO and PhD-SNP tools to identify disease-associated SNPs and predict the connection between nsSNPs in AKT2 and IR and T2D, exploring their impact on AKT2 structure and function and IR and T2D development. The limited set of polymorphisms that have undergone filtration and reduction were subsequently evaluated to determine their association with diseases. Subsequently, 75 nsSNPs with a high level of confidence were submitted to SNP&GO and PhD-SNP. Both individuals computed a numerical value to classify every nsSNP as either benign or pathogenic. A single nsSNP was deemed neutral if its prediction score was less than 0.5, whereas a score greater than 0.5 indicated that the SNP was disease-causing. Out of a total of 75 nsSNPs, 47 variants were determined to have a neutral effect, while the remaining 28 nsSNPs were identified as having a causal association with disease. To reduce the number of nsSNPs, a cut off value of We used the I-Mutant and MuPro servers to try to figure out what would happen to the structure of 20 possible nsSNPs. Based on the results, 20 nsSNPs were thought to stop proteins from working by making them less stable. Moreover, as per the Mut-pro server, it was indicated that only 3 nsSNP out of 20 showed an increase on the protein’s functionality due to structure impact. The findings are presented in We utilized The Mut-pred web server to predict the pathogenicity and underlying functional changes caused by nsSNPs on the AKT2 protein. This approach aids in understanding the potential impact of these genetic variations on protein function and their association with disease, such as T2D related to insulin resistance. The Mut-pred web server offers valuable insights into the molecular mechanisms linking nsSNPs in AKT2 to disease susceptibility and provides a foundation for further investigation. The Mut-pred web server generates outcomes that aid in predicting the probable influence of missense mutations on protein function that is linked with diseases. Furthermore, Mut-pred provides details regarding the particular characteristics of the protein that are anticipated to be impacted by the mutation, encompassing solvent accessibility, secondary structure, and sequence conservation. According to the results presented in The ConSurf server was utilized to investigate the preservation of 20 nsSNPs that are associated with T2D. As per the findings, a set of three nsSNPs exhibited variability, whereas four nsSNPs demonstrated a modest conservation level. A set of nsSNPs that were determined to be conserved was analyzed, revealing that seven of them displayed both structural and spatial conservation, and they were located in buried regions. Six nsSNPs were predicted to be functionally conserved and exposed. The ConSurf tool employs chromatic schemes to represent the extent of sequence conservation and offers predictions about the nature and location of modifications on a numerical scale ranging from 1 to 9. Through these simulations, we gained valuable insights into the dynamic behavior of the protein, its interactions, and conformational changes that may influence AKT2 structure and function that may lead IR and T2D.Solvents and ions were then balanced around the protein structures after energy minimization. NVT (thermostat) and NPT (bariostat) equilibrations were carried out to make sure that the molecules were oriented correctly within the structures. In an isothermal–isobaric ensemble, these two phases were used to stabilize the systems’ temperature and pressure, respectively. The computational tool known as “gms/grompp” was utilized to perform calculations for the NVT and NPT ensembles of the system. The equilibrated process involves temperature, pressure, and density, leading to the establishment of a density plot from NPT. https://www.gromacs.org/. The wildtype is visually represented by the colour purple, while the A179T mutant is depicted in green, and the L183Q mutant is represented by the colour blue.The pressure–time graph  indicate−3, with minor fluctuations. However, it is noteworthy that the mutant A179T exhibited a lower initial density value of 985 kg/m−3. The L183Q mutant displays analogous fluctuations in pattern to the native AKT2 and mutant A179T; however, it maintains a higher value of 1020.5 Kg/m−3.The average density value observed for both the native protein and the mutant variant was 1015 kg/mComparative analysis of the RMSF and RMSD of AKT2 wildtype and its A179T and L183Q mutations was performed. To compare and depict the structural and functional behavior of the wildtype AKT2 gene and its two mutant forms, A179T and L183Q, the current work used a molecular dynamics simulation method. The RMSD was used to evaluate the trajectories of the native and mutant forms of the AKT2 protein. The trajectory file for the C-alpha backbone least-squares fit model and g_rms were used to construct the RMSD graphs shown in The initial conformations of the three structures exhibited close proximity; however, a sudden deviation was observed at 10 ps, resulting in an RMSD value of 0.11 nm for the two mutants.The wildtype structure achieved its highest RMSD value at 8 ps, while the A179T and L183Q mutants reached their maximum values at 14 ps and 15 ps, respectively. The RMSD graph comparing the wildtype AKT2 gene to its A179T and L183Q mutants, which were aligned to the backbone, indicated that the wildtype AKT2 gene exhibited greater stability than the A179T and L183Q mutants. Subsequently, the aforementioned mutants have been observed to impact the protein dynamics of the wildtype AKT2. This is evidenced by alterations in the RMSD graph of the backbone of the mutants, thereby providing a suitable basis for subsequent analysis. The analysis of RMSF values of native and mutant residues was conducted to ascertain the impact of mutations on the dynamic behavior at the atomic level B. The RM2 to 317.5 nm2, with an average of 315 nm2. The SASA values for A179T  and Solvent Accessibility Surface Area (SASA) of both the wildtype and the A179T and L183Q mutations in AKT2 is vital in understanding their impact on AKT2 structure and function. These structural metrics offer insights into the compactness and exposure of amino acids, shedding light on potential conformational changes that contribute to the complex relationship between these mutations and IR and T2D. The concepts of radius of gyration and SASA are of interest in various fields of study. The concept of Rg pertains to the root-mean-square distance that all atoms occupy from their shared center, as determined by their mass and weight. This makes it easier to understand all of the components of protein analysis. or A179T B and L18or A179T B were obThe prevalence of DM is increasing worldwide and it has a serious socioeconomic burden . T2D repThe serine/threonine kinase AKT2, also called protein kinase B (PKB), is implicated in the regulation of insulin signaling pathway. Insulin has important roles in the metabolism of glucose, proteins, and fats . AKT2 isOur study identified 20 variants as the most detrimental effect based on the collective outcomes of various analytical tools. Then, two potential mutants  were selected from the 20 nsSNPs deemed to be most pathogenic mutants for further investigation through molecular dynamics (MD) simulation. The identified mutations were observed to modify the protein’s stability and function properties, which may have an impact on its biological activity. A molecular dynamics simulation was performed to evaluate the disparities in protein dynamics between the wildtype and mutant AKT2 proteins. The simulation was conducted over a period of 100 ps, during which a range of parameters, including temperature, pressure, density, RMSD, RMSF, SASA, and Rg, were evaluated and scrutinized. Comparisons were performed to investigate the functional characteristics and stability of the protein structures. Upon conducting minimization, marginal variances in temperature, pressure, and density were observed between the native and mutant conformations over the course of the simulation, although the mutant p.L183Q revealed slight increases in these parameters. The RMSD plots of backbone atoms indicated that the wildtype structure demonstrated a higher degree of stability in comparison to the mutants. The RMSF plots indicated that the native and mutant structures exhibited the greatest level of flexibility. The RMSF calculations, which were averaged across the residual elements and trajectories, indicated that the fluctuation of atoms within the structures was notably greater in the first third. The investigation encompassed an analysis of the radius of gyration and SASA to ascertain the impact of the mutant protein structure’s unfolding on the solubility of surface area and the activity of the protein. A higher Rg value is indicative of reduced stability. The present findings suggest that the native structure exhibited a narrower range of Rg values in comparison to the mutants. Moreover, the solvent accessible surface area of the wildtype AKT2 is different from the mutants of the AKT2 protein. This may indicate that the two mutations (p.A179T and p.L183Q) lead to a reduction in AKT2 stability and functionality. Alanine (A) is the second simplest amino acid; it is hydrophobic. Meanwhile, threonine contains a hydroxyl group; it is a hydrophilic . In p.A1In this study, we examined the potential association of non-synonymous SNP (nsSNPs) in the AKT2 gene with IR and T2D. Results indicated that two mutations, p.A179T and p.L183Q, may cause a reduction in AKT2 protein stability and functionality which can be a cause for IR, metabolic syndrome and T2D. These results require further verification in protein functional, PPI and large case–control studies. After being verified, these results can be used in genetic testing for T2D in risk stratification for treatment or prevention strategies.


Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction. There remains considerable untapped potential in these developed models, such as whether the prediction performance can be further improved by integrating different models to further improve the accuracy of prediction. Furthermore, how to utilize large-scale models for prediction methods of mutational effects on quantifiable properties of proteins due to the nonlinear relationship between protein fitness and the quantification of specific functionalities has yet to be explored thoroughly. In this study, we propose an ensemble learning approach for predicting mutational effects of proteins integrating protein sequence features extracted from multiple large protein language models, as well as evolutionarily coupled features extracted in homologous sequences, while comparing the differences between linear regression and deep learning models in mapping these features to quantifiable functional changes. We tested our approach on a dataset of 17 protein deep mutation scans and indicated that the integrated approach together with linear regression enables the models to have higher prediction accuracy and generalization. Moreover, we further illustrated the reliability of the integrated approach by exploring the differences in the predictive performance of the models across species and protein sequence lengths, as well as by visualizing clustering of ensemble and non-ensemble features. Proteins play a pivotal role in the fields of life sciences, health, and chemical engineering. They can serve as both integral components of the human body  and carrThe fields of protein engineering, protein biochemistry, and evolutionary biology have long been interested in the question of where and how to mutate a protein of interest to make the mutated protein more suitable for practical applications. Most of the time, researchers prefer to employ means including directed evolution and rational design to obtain the desired answers and products . DirecteRecently, the field of protein engineering has experienced a significant impact from the advancements in machine learning. To address longstanding challenges, various related methods and tools have been proposed. For instance, AlphaFold , a highlWith the continuous deepening of research on protein language models, an increasing number of protein language models with different architectures and scales have been proposed, and the applications using these models are increasing accordingly. For example, Ali Madani et al.  demonstrl et al.  used prol et al. . The exil et al. ,30. As al et al.  to the cl et al. . Moreovel et al. ,23 and Pl et al. ,25. Howel et al.  seems tol et al. ,35,36,37In this study, we propose an ensemble learning approach for protein mutation effect prediction tasks. This approach employs pre-trained protein language models of different scales trained on different various databases in order to take advantage of the potential complementarity among the feature information extracted by different models. This approach aims to better simulate the evolutionary relationships between species, capturing the semantic grammar in protein sequences as well as background knowledge in association with structure and stability. Furthermore, we incorporate local evolutionary context into the ensemble representation, inspired by the method of explicitly capturing residue interactions using CCMPred  in ECNetTo demonstrate the utility of our approach, we tested it on S17  characteristics and properties of deep mutational scanning (DMS) data, including the number and distribution of conserved regions among homologous sequences. The integration approach can partially compensate for the limited generalizability of models arising from these effects.In the process of utilizing artificial intelligence for protein engineering tasks, a common scenario is the scarcity of data, making it essential to investigate the influence of data quantity on model prediction. This matter has been thoroughly examined in the research conducted by Barbero et al. . In mostAlthough the proposed approach in this study generally brings about favorable effects, there are instances where undesired performance can be observed. For instance, when testing on the A0A2Z5U3Z0  dataset The training and testing datasets utilized in this study were sourced from ProteinGym , a comprInclusion of all taxa present in the full dataset ;Even distribution of protein sequence lengths (100aa–1000aa);A wide range of sample sizes .After careful consideration of computational and time costs, we selected a portion of the substitution benchmark as the primary experimental dataset. Although ProteinGym includes measurement data for indel mutations, its quantity is less than a tenth of the substitution data. Moreover, indel mutations are often more complex than substitution mutations. Therefore, investigating complex systems with limited data may not provide a sufficiently objective representation of reality. To capture a more comprehensive representation of the complete dataset, we subset the substitution benchmark based on the following criteria:With these criteria, we selected 17 DMS datasets, referred to as S17 (Substitution 17) in this study. It can be expected that S17 will accurately reflect the distribution of the complete dataset while minimizing computational and time costs. This subset will facilitate a comprehensive comparison of differences between different models. When addressing highly complex problems, it is often of great necessity to employ larger, more parameter-rich models to effectively capture the desired information and knowledge . HoweverPre-trained protein language models are trained using extensive protein sequence databases, and different models are obtained by training on different datasets, with the extensively utilized UniRef ,23 serieIn this study, we primarily concentrated on two pre-trained protein language models: Tranception L  and TapeTranception L stands out as the protein language model with the largest parameter count, which boasts approximately 700M parameters, making it the most expansive model observed in this study. Its architecture encompasses 20 attention heads  and a toTape, a study that evaluates protein embedding tasks, encompasses numerous popular deep learning models, including BERT-base (a transformer model), LSTM , and ResWhile no modifications were applied to the Tape-transformer model, some adjustments were implemented for Tranception L. As Tranception L is originally an unsupervised model designed for the prediction of protein mutation effects, we excluded the inference part of the model. Instead, we retained the output of the model from the “hidden states” component, which served as our feature extractor, converting protein sequences into representations.Regarding hyperparameter settings, we utilized the default hyperparameters for the Tape-transformer model. In addition, for Tranception L, as each amino acid character is encoded as a 1280-dimensional vector, and with the model boasting over 700M parameters, specific adjustments were essential for efficient execution on a single NVIDIA 3090 GPU. We experimented with different “batch size” values to accommodate protein sequences of varying lengths. Ultimately, we found that a batch size of 16 was suitable for protein sequences of all lengths. Moreover, we set the “scoring window” to the “optimal” option, accommodating longer sequence lengths.To harness the diverse capabilities of various protein language models and effectively model protein sequences in relation to their functions, an ensemble learning approach is employed in this work b. InitiaIn order to ensure compatibility with subsequent supervised models, the problem of different dimensions of the feature vectors generated for each amino acid letter in different pre-trained models has to be considered and solved. This entails operations such as feature dimensionality reduction or feature concatenation, enabling the creation of a matrix composed of feature vectors with varying dimensions. Using this approach, we preserve the sequence representation produced by each model while retaining the complete feature information of each amino acid in the sequence. Furthermore, based on the strategy employed in ECNet, we leverage CCMPred  to prediCCMPred  is an alTo conclude, by employing a multi-model integration strategy, we present a novel approach for protein mutation effect prediction in this study. This approach leverages the unique architectural advantages of different models and the synergistic effects of diverse training data, allowing us to unlock the full potential of existing large-scale models. To evaluate the performance of our proposed approach, we conduct comprehensive benchmarking and ablation experiments on the carefully selected deep mutation scanning dataset, S17. Our experimental results demonstrate that the models utilizing the integrated approach consistently outperform both the previously published methods and models discussed in this study, with statistically superior average predictive performance. Moreover, these promising outcomes underscore the potential of our approach to significantly enhance the workflow of directed evolution, providing valuable assistance in the development of novel proteins with improved functional characteristics.


Protein containers play important roles in nanotechnology applications, including diagnostics, drug delivery, and vaccination. These applications require programmable control over container size and stoichiometry. The seminal Caspar–Klug approach for the classification of virus architecture predicts nanocontainer geometry if protein subunits exhibit similar interaction types across the capsid surface. However, in many nanoparticles, there are identical protein subunits that do not interact with other subunits in the same way, thus presenting gaps in the container surface. We demonstrate that in these cases, the interaction network between the assembly units can be used to gain predictive control over the spectrum of particle morphologies. This paves the way to programmable control over particle polymorphism and informs the design of nanoparticles for specific applications. Protein nanoparticles play pivotal roles in many areas of bionanotechnology, including drug delivery, vaccination, and diagnostics. These technologies require control over the distinct particle morphologies that protein nanocontainers can adopt during self-assembly from their constituent protein components. The geometric construction principle of virus-derived protein cages is by now fairly well understood by analogy to viral protein shells in terms of Caspar and Klug’s quasi-equivalence principle. However, many artificial, or genetically modified, protein containers exhibit varying degrees of quasi-equivalence in the interactions between identical protein subunits. They can also contain a subset of protein subunits that do not participate in interactions with other assembly units, called capsomers, leading to gaps in the particle surface. We introduce a method that exploits information on the local interactions between the capsomers to infer the geometric construction principle of these nanoparticle architectures. The predictive power of this approach is demonstrated here for a prominent system in nanotechnology, the AaLS pentamer. Our method not only rationalises hitherto discovered cage structures but also predicts geometrically viable options that have not yet been observed. The classification of nanoparticle architecture based on the geometric properties of the interaction network closes a gap in our current understanding of protein container structure and can be widely applied in protein nanotechnology, paving the way to programmable control over particle polymorphism. Protein containers are ubiquitous in nature. Prominent examples are the viral protein shells, called capsids, that provide protection and transport for viral genomes between rounds of infection. Protein cages also serve vital functions in bacteria as microcompartments, or in prokaryotic cells, where encapsulins, ferritin, and lumazine synthase cages facilitate catalysis , intraceViruses have evolved mechanisms to assemble specific geometric designs with high fidelity and efficiency. The vast majority of virus architectures exhibit icosahedral symmetry as a consequence of the principle of genetic economy , as this1219SI Appendix, Fig. S1). In the considered and some other cases, the tiles in the surface lattice are in a one-to-one correspondence with the biological units. We therefore represent bacteriophage MS2, which assembles from protein dimers, by a rhomb tiling  theory was the first to propose quasi-equivalence as a geometric design principle for the structural organisation of icosahedral viruses . CK theob tiling B, thus cb tiling .A further complication arises for human papillomavirus (HPV), a capsid formed from 72 identical pentamers. Due to the crystallographic restriction  there isAquifex aeolicus lumazine synthase (AaLS)   . However, in such mathematical representations, there is no clear biological interpretation of the lattice unit in terms of individual capsomers or interactions between protein subunits. Therefore, such an approach does not allow prediction of other possible cages made from AaLS pentamers.In this case, neither capsomers nor interactions between individual CPs can be represented by tiles in a meaningful way. Of course, the particle surface can always be represented as a tessellation in terms of multiple copies of the fundamental domain  of the underlying symmetry group, in this case consisting of three Voronoi cells , and then, the cubic surface is embedded into a planar tiling that continues the interaction network periodically in the plane. This tiling can then be used to construct models for other particle types via different embeddings of the cubic surface, akin to the embedding of icosahedral surfaces into hexagonal lattices in Caspar–Klug theory.In order to achieve this, we construct the interaction network between capsomers (rather than between their constituent protein subunits) . For thiLeft) provides a simple example of this interaction network approach. Representing each of its 24 proteins as a node (Left) and drawing connections between interacting proteins, results in the interaction network (shown in black). By aligning the 4-fold and 3-fold symmetry axes of the particle with those of a reference cube (Middle), the interaction network can be mapped onto the cubic surface. The latter is then embedded into a planar tiling by “unfolding” the cubic surface in the plane (Right). Any other particles assembled from the same protein units should exhibit similar local interactions. From a mathematical point of view, this means that their interaction networks can be constructed by working backward from the planar tiling. In particular, any other planar embedding of the cubic surface, obtained via rescaling and reorienting the surface in the plane such that the symmetry axes of the cube again coincide with those of the tiling , then presents an alternative particle layout. To reconstruct the biological model, vertices have to be replaced by biological units, oriented such that interacting units meet along the edges of the interaction network  is important in nanotechnology. It can be used in the context of kinetic models to compute relative ratios of different particle morphologies for different experimental conditions, thus opening up the opportunity to tune experiments to favour production of desired particle types . It alsoGiven a nanoparticle of interest, the first step consists in computing its interaction network. In AaLS-based nanoparticles, pentamers and their genetic variants self-assemble into a spectrum of different particles with tetrahedral and icosahedral symmetry . RepreseThe next step in the analysis is to identify the distinct geometric shapes in the interaction network. In this case, there are two types: interactions in groups of three, corresponding to triangles, and interactions in groups of 6, corresponding to nonregular hexagons. The latter, called squashed hexagons in the following because of their characteristic shapes, can be divided into two triangles and one square as indicated by dashed red lines in SI Appendix, Fig. S4 and Text). In the case of AaLS-based nanoparticles, we therefore consider The interaction network of AaLS pentamer cages can therefore be embedded into planar tilings made of triangles and squashed hexagons. For other nanoparticles, the nature of tiles will depend on the local interaction patterns and may therefore differ. However, as nanoparticles self-assembling from  protein units exhibit only a limited spectrum of distinct local interaction patterns between neighbouring capsomers, planar tilings associated with their interaction networks must all be SI Appendix, Text), 140 of which are made entirely of triangles and squares  from further consideration . Three of the remaining seven tilings  contain local 6-fold symmetry axes and therefore must also be excluded as they would require vertices representing pentamers to be positioned on a 6-fold symmetry axis . In summary, only four tilings fulfill all necessary criteria to allow for the embedding of an AaLS interaction network. These are the triangular ( squares –37. The 2·4·3·4) C.Left). There are four inequivalent options of organising triangles and squashed hexagons around these axes , and vice versa, resulting in the tiling called SI Appendix, Fig. S8A). In each case, a type-4 square is required next, and then the tessellation must be continued with alternating type-3/type-4 squares , leading to the tilings SI Appendix, Fig. S7B), SI Appendix, Fig. S8D). Thus, modulo handedness, the snub square tiling can be divided into squashed hexagons and triangles in precisely three inequivalent ways, corresponding to the tilings Starting with a type-1 square, the only option is to continue the tiling with type-2 squares . For this, a planar representation of the cubic surface must be embedded into the tiling such that its corners align with the centres of appropriately spaced squares in the tiling. For example, allocating the vertices of the cubic surface to the centres of adjacent coloured squares in the Left), and then mapping these onto the vertices of a cube (Middle), generates a model for an AaLS cage made of 24 pentamers (Right). This particle has been observed in the self-assembly of AaLS pentamers . As vertices represent pentamers, this would generate a ring-like interaction between four AaLS pentamers in the particle surface, which is a local interaction type that has not been observed in any AaLS cage to date. We therefore exclude it from our classification. There are thus no other biologically viable AaLS cage models that can be constructed from this tiling. Similarly, both It is not possible to embed the surface of a cube into this tiling in any other way without mapping squares in the tiling onto the faces of the cube  would necessarily contain a square, i.e., a group of four pentamers, and we again reason that this would not be a biologically viable option.The 2-uniform tiling . Thus, the interaction network approach provides a unifying framework for the modelling of virus and nanoparticle architecture alike. It is applicable in bionanotechnology for the classification of nanocontainer architecture and can be built as local constraints into programmes like Rosetta to support nanoparticle design. The geometric models also open up broad avenues for the study of the biophysical properties of protein cages, such as their propensity for fragmentation and cargo release Click here for additional data file.


Protein complexes are groups of polypeptide chains linked by non-covalent protein–protein interactions, which play important roles in biological systems and perform numerous functions, including DNA transcription, mRNA translation, and signal transduction. In the past decade, a number of computational methods have been developed to identify protein complexes from protein interaction networks by mining dense subnetworks or subgraphs.PCGAN, meaning identifying Protein Complexes by GAN. With the help of some real complexes as training samples, our method can learn a model to generate new complexes from a protein interaction network. To effectively support model training and testing, we construct two more comprehensive and reliable protein interaction networks and a larger gold standard complex set by merging existing ones of the same organism (including human and yeast). Extensive comparison studies indicate that our method is superior to existing protein complex identification methods in terms of various performance metrics. Furthermore, functional enrichment analysis shows that the identified complexes are of high biological significance, which indicates that these generated protein complexes are very possibly real complexes.In this article, different from the existing works, we propose a novel approach for this task based on generative adversarial networks, which is called https://github.com/yul-pan/PCGAN. V indicates the set of proteins (or nodes) and E is the set of interactions (or edges). A comprehensive PIN containing more proteins and interactions is beneficial to accurately and completely identify complexes.Protein complexes (PCs) are composed of interacting proteins, which play important roles in various cell functions, including signal transduction, protein degradation, and mRNA translation  assays aIn the past decade, a number of computational methods have been proposed to identify PCs from PINs . With the help of some known complexes as training samples, PCGAN first learns the characteristics of PCs, and then generates new complexes. Specifically, PCGAN trains two models: a generator for generating PCs, and a discriminator for distinguishing the generated PCs from real ones. The competition learning between the generator and the discriminator promotes the two models to improve their capabilities until the generated complexes are indistinguishable from the real ones. Furthermore, to improve the performance of complex identification, we construct two more comprehensive PINs and a larger gold standard complex (GC) dataset by merging the existing datasets.In this work, we propose a novel approach PCGAN to identify PCs from PINs. It is a generative approach, which is quite different from the existing works that are mainly based on clustering analysis over PINs. Here, PCGAN means identifying We conduct extensive experiments to evaluate the proposed method. Our experimental results indicate that PCGAN is superior to the existing methods. We also perform function enrichment analysis on the generated complexes, which shows that our generated PCs are of high biological significance, i.e. they are very possibly real complexes.To the best of our knowledge, this is the first generative work of complex identification. Our method pioneers a new direction of PC identification.The datasets used in this article consist of two parts: PINs and gold standard PC sets, of two organisms (human and yeast). The detailed description of the datasets is as follows:CPIN-H, which contains 225 642 PPIs over 16 600 human proteins. As for yeast, we used five widely used PINs, including Collins . CGold-Y.We downloaded the latest version CORUM 3.0  complex is chosen to join the current complex or terminate the expansion of the current (intermediate) complex. As each node in the PIN will be selected for complex generation, the order will not impact the final result. Next, the true complexes and the generated complexes are input into the discriminator, which returns the gradient update to the generator, to make it generate more high-quality complexes. Finally, the proteins of complexes in the test set are input into the trained generator to generate candidate PCs. And the final PC set is obtained by removing redundant complexes according to the complex match rate x) represent column vectors, and uppercase letters (e.g. X) denote sets.atematch . As an eV is a set of vertices (i.e. proteins) and E is the set of edges (i.e. interactions). The discriminator PC represents the internal node set of the PC, and NPC represents its neighbor node set.A PIN can be represented by an undirected graph v is a node in the PIN, and v is evaluated as follows:above, v, and v in C, and l-th layer. The final representation of v is obtained by concatenating the representations of each layer: C by the readout function to generate the representation of C: W means the parameter set of The discriminator is a “three”-layer GIN, and the initial features of nodes are evaluated as follows:where t-th step  The design of policy t-th step, the augmented initial feature of node v is as follows:v contains the information of the current complex and the seed node. Then, the node feature is processed by a graph neural network (GNN) model. Here, we use the Graph Pointer Network with incremental updates (iGPN) model  model , to procThen, we design the action representation Specifically, the STOP action representation is defined as follows:Q = where 2) Optimizing the generator with policy gradient.Q isHere, the generator is trained by the policy gradient strategy (3) Pre-training and teacher forcing.Pre-training can provide a well initial model, and teacher forcing can effectively prevent the model from deteriorating and getting stuck in some training batches with the help of supervised learning in each reinforcement learning step. Here, we use maximum log likelihood estimation (MLE) for model pre-training and do teacher forcing as described in 4) Algorithm.) pre-training the generator Ta using MLE (Line 1); (ii) iteratively training the discriminator Ta. (iii) Generating complexes one by one with the trained generator on the testing set Te (Lines 16–19). (iv) Removing duplicate complexes from the generated complexes (Lines 20–23).The overall PCGAN algorithm is outlined in Algorithm 1 PCGANRequire: PIN Ta, Complex testing set TeTa by using MLE1: Pre-train for epoch = 1,… do2:  for D_step = 1,… do3:   Sample batch 4:   Sample seeds 5:   Generate protein complexes 6:   Train 7:  end for8:  for G_step = 1,… do9:   Sample batch 10:   Teacher-force 11:   Sample seeds 12:   Train 13:  end for14: end for15: for each protein u in Te16:  Generate protein complex 17:  Protein complex set 18: end for19: for any two complexes PCs20:  ifthen21:   Remove the PCs22: end for23: return The final protein complex set PCs24: F-measure,” and “maximum matching ratio” (MMR). Before describing these metrics, we first introduce F-measure” is the harmonic mean of “Recall” and “Precision.”To fairly compare the performance of various methods, we use four commonly used metrics, including “Recall,” “Precision,” “MMR  are used as the background set and we ignore the annotations.Here, g: Profiler  different high-throughput experiments tend to find different types of PPIs  a PIN is a dynamic network, PPIs change in cell at any time  current PPI experimental techniques may produce false-positive interactions  in the merged gold standard set as testing samples. Specifically, for human, we used 500 complexes in CORUM as the training set, 30% of the training set as the verification set, and the rest as the test set. For yeast, we used 300 in CGold-Y as the training set, 30% of the training set as the verification set, and the rest as the test set. All methods identify PCs from CPIN-H (for human) and CPIN-Y (for yeast), and compare the predicted complexes with the gold standard test set.F-measure and MMR, which indicates that our method is more effective in identifying PCs than the other methods. For yeast, PCGAN is superior to the other methods in terms of all metrics. In summary, the results above show that PCGAN is effective in identifying PCs, it can generate high-quality complexes from both yeast and human PINs.P-value. The smaller the P-value of a complex is, the more significant its biological function is. The P-value threshold of significance is set to .05 by default. P-values. On the other hand, these three methods identify much less complexes than our method PCGAN. In summary, PCGAN can effectively generate PCs of high biological significance.Due to the incompleteness of existing gold standard sets, which cannot validate all generated complexes. Thus, in this article, functional enrichment analysis is employed to verify the effectiveness of the proposed method. Concretely, we used g:Profiler  extending the proposed method to identify PCs from PINs with biological features; and (ii) designing more advanced deep generative models (e.g. diffusion models) for PC generation.Accurate identification of PCs from PINs is an important research topic in computational biology (btad473_Supplementary_DataClick here for additional data file.


Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence. Protein is the term proposed by the Swedish chemist Jacob Berzelius to compounds containing nitrogen and constituted by a combination of amino acids linked by a bond, called peptide bond . They arExperimentally, tertiary protein structures are resolved by X-ray crystallography, nuclear magnetic resonance and electron cryomicroscopy (cryo-EM). Nevertheless, those techniques are complex, time consuming, expensive, and often the structure is not in its native form. Under these constrains, it is no surprise that the number of proteins with resolved tertiary structures is small  compared to the large number of proteins sequence known . This is an open challenge waiting for innovative ways to develop novel protein structure prediction approaches such as the ones using computationally models to predict the three dimensional protein structures starting from a polypeptide sequence .Several algorithms and web servers have been developed with the aim to improve the protein structure prediction. The relevance of these efforts are underscored by the different application that shall be affected including rational drug design, mutational studies and structural comparison, evolutionary analysis, folding studies. Here, we provide an overview of the methods developed to perform protein structure prediction to compare with AlphaFold2, which combines neural networks and homology modeling to generate models that may have experimental accuracy in a growing number of examples. We also discuss its applicability, limitations and efforts to improve the overall algorithm’s performance.ab initio (a FM methodology), threading/fold recognition (a TBM methodology), and homology (a TBM methodology)  and free modeling (FM), considering the use or not of templates , even thodology) . Despiteodology) .ab initio approaches are based on the thermodynamics hypothesis that the native protein structure presents the lowest free energy possible , reaching a summed z-score of 52.8 sus 48.2 . Even wisus 48.2 .The first version of AlphaFold used deep learning to predict the protein structure, demonstrating that it is possible to learn protein specific potential by training a neural network giving only the protein sequence. It contains a convolutional neural network that is trained by PDB structures to predict the distances between residues, creating distograms. From the amino acid sequence of the target protein, the neural network predicts a distogram based on multiple sequence alignment (MSA) features, in which a separate output from prediction network predicts the probability of backbone torsion distribution. The combined potential obtained by both ends is then optimized by gradient descent, predicting the protein structure itself .versus the 2.8Å from the next best performing method. Moreover, AlphaFold2 scored 244.0 in summed z-scores compared with 90.8 for the next closest group  among prediction and experimental backbone structures of 0.8Å st group . The greIt is important to emphasize that AlphaFold2 contrasts considerably from the first version of AlphaFold. The authors attribute the high performance of AlphaFold2 by “incorporating novel neural networks architectures and training procedures based on the evolutionary, physical and geometric constraints of protein structures” . AlphaFoThe evoformer extracts information from MSA and templates, exchanging information between them in flows back and forth throughout the network, improving the assessment of the MSA, that in turns modifies the protein structures hypothesized by the templates, allowing the MSA and templates in the correct “embedding space”. It consists of two transformers, networks that use attention to boost the speed with which a model can be trained, each of them specialized for a type of data, MSA or pair representations, with a clear communication channel between them. This allows the MSA transformer attention mechanism to incorporate information from the pair representation, adding a bias term from it, augmenting the attention mechanism and allowing it to pinpoint interacting pairs of residues. The pair representation transformer also works in a similar way, but includes an attention to terms of triangles of residues. The structure module, that also contains an attention architecture, uses both representations to prioritize the orientation of the protein backbone, considering the residue rotations and translations, localizing each side chain of each residue in highly constrained within a frame, followed by local refinement and minimization by gradient descent .https://github.com/deepmind/alphafold).Protein-protein interactions are the basis of the biological process, and high-resolution structural characterization of these interactions give rise to insights of their molecular mechanisms and function, as well as direct the design of new drugs that are able to modulate these molecular pathways. Alphafold2 has been used to predict protein-protein interaction, using flexible linkers or artificial gaps and, in general, it predicted heterodimeric protein complexes accurately, exceeding docking approaches usually used in these analysis . Neverthhttps://alphafold.ebi.ac.uk) was created, making available over 360,000 predicted structures from 21 organism proteomes , the AlphaFold Protein Structure Database , which uses the amino acids sequence only and is able to learn evolutionary, structural and functional patterns derived from sequences available in databases, predicting a structural conformation. The idea behind those methods is that the amino acids correspond to words/tokens and proteins to sentences in NLP, assuming that similar semantics come from amino acids that occur in similar contexts.There are three approaches used in language models: autoregressive, bidirectional, and masked. The first takes into count the previous tokens (amino acids) to predict the probability of a token, the second considers the previous and following tokens independently to estimate the probability of a token, and the last model considers all tokens in a sequence and replaces each token with a mask token. A synthesis of the recent advances in protein language modeling and their applications to protein prediction problems can be found elsewhere .versus 0.85 using AlphaFold2 on CASP14). However, when evaluating AlphaFold2 without MSA, using only the amino acid sequence, ESMFold performed better (0.68 versus 0.37 using AlphaFold2 on Casp14). Moreover, it presented an accuracy comparable to AlphaFold2 for structures predicted with high confidence, demonstrating a median all-atom RMSD of 1.91Å and a backbone RMSD of 1.33Å, reaching similar experimental-level accuracy. Finally, this approach also demonstrated a significant improvement in prediction speed, since it does not require the construction of MSA. Using this approach, the authors presented the ESM Metagenomic Atlas, where they predicted more than 617 million structures from metagenomic databases, in which 225 million structures were predicted with high confidence, including those that are novel  (EMBER2 is a protein language-model developed by labFold) . HoweverlabFold) . Again, These new approaches highlight the powerful capacity of language models to identify evolutionary, structural and functional patterns from massive protein sequence databases to solve protein prediction problem, improving prediction speed and requiring less computational power. It is expected that those approaches will develop and gain accuracy with the inclusion of biological knowledge and multi task learning.Three-dimensional protein structure determination is important to elucidate the protein function, being critical to understanding biological processes and addressing human health and life science problems in general. Due to the difficulty of determining protein structures by experimental methods, their predictions have been one of the central problems of the scientific community. The advent of AlphaFold2 and the release of millions of protein structures predicted with high accuracy and available in AlphaFold DB allowed an unprecedented expansion of different research fields in life science, impacting the most biological sciences, followed by biochemistry and cell biology, genetics, medical and health sciences and chemical sciences .Protein structure prediction can be applied from understanding the interaction between pathogen and host, how pathogens survive and reproduce, and why they are resistant to certain drugs used, to the development of new and more efficient drugs, reducing the cost in drug discovery and development, as well as the development of new and improved vaccines . The devThe recent advances in protein structure prediction have contributed to improve protein folding issues. The emergence of AlphaFold2 has taken the problem of protein structure prediction to another level, reaching similar experimental-level accuracy in some cases. Nevertheless, improvements are needed to overcome the limitation to predict novel structures, intrinsically disordered regions and loops, the ability to predict only a single conformer without ligands and still present inconsistencies in its models, and the inability to predict the impact of missense mutation on protein structure. These limitations, ultimately, are important drawbacks to expand the use of AlphaFold in life science.Efforts are in progress to improve AlphaFold performance and models. Simultaneously, new strategies using protein language models are arising to compete with AlphaFold2 in terms of performance and accuracy and to overcome some of its limitations. These strategies, more specific ESMfold, offers an opportunity to identify new proteins and novel functions, allowing the identification of new species, including microorganisms and viruses that endanger human health, as well as those that offer solution to mitigate environmental problems, such as the degradation of polluting and the development of transgenic microorganisms for more efficient product production. For this, ESM Metagenomic Atlas is an important 3D protein structures resource to be deeply investigated by the scientific community . Finally