Science, Hicks et al. utilized Mass spectrometry integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS) to explore protein-metabolite interactome and have revealed previously unknown regulation for lactate dehydrogenase.1 This report brings an exciting new approach to address a long-standing challenge in biology.In a recent study published in Cellular functions are orchestrated by an interactive network of molecular constituents, the physical interactions between proteins and other molecular entities, such as DNA, RNA, and metabolites. Like basic vocabulary for the book of life, these interactions are the fundamental mechanisms for complex physiology and diseases. Over the past two decades, the development of high-throughput sequencing and mass-spec based analysis tools helped to unlock the molecular identities and their interactions at unprecedented scales of complexity, yielding rapidly advanced understanding to protein-protein, protein-DNA and protein-RNA interactions. In contrast, the protein-metabolite interaction (PMI) remains poorly defined. Increasing evidence supports the importance of metabolites not just as substrates for metabolic reactions, but also as signaling molecules with potent functional impact through their interacting proteins. Therefore, developing unbiased approaches to systematically and comprehensively determine PMI network is critically needed to fill this important gap.Escherichia coli and revealed functional and structural principles of metabolite-protein communication.2 One unique feature of LiP-SMap approach is that it will not only map protein-metabolite interactions, but also provides information on the potential ligand-binding sites on the targeted proteins.One recent development is LiP-SMap  platform. It is a mass-spectrometry based technology taking advantage of altered protein sensitivity to proteolysis upon binding with metabolites. Whole cell lysates extracted under undenatured conditions are treated with or without a metabolite. Binding of the metabolite changes the proteolytic susceptibility of its cognate binding protein, allowing it to be differentiated from the non-binding proteins. Using LiP-SMap, Piazza et al. showed its potency by uncovering known and novel PMI interactions in 3 This report showcases the advantage of PROMIS for comprehensive profiling of PMIs in an unbiased fashion, however, the detection may be limited by the native abundance of the metabolites and their target proteins.Another large-scale mapping tool is PROMIS (Protein-Metabolite Interactions) platform which combines size-exclusion chromatography with proteomic and metabolomic methods to detect interactions between untagged proteins and metabolites based on their co-segregation profiles. In a recent publication by Luzarowski et al., dividing yeast cells were used as a source for endogenous protein-protein and protein-metabolite complexes. The complexes were fractionated using size exclusion chromatography followed by liquid chromatography-mass spectrometry to detect both proteins and metabolites from the same fraction. A dataset for PMI based on co-segregation from chromatography was established for the entire proteome and metabolome in Saccharomyces cerevisiae.1 The concept of MIDAS is built upon on the biophysical principle of equilibrium dialysis  cycle as well as serine biosynthetic pathway. A total of 830 putative PMIs were identified, including many previously unknown ones. Beyond these individual interactions, some global features begin to emerge. As expected, structurally and functionally related proteins frequently show similar metabolite interaction profiles, as demonstrated for lactate dehydrogenase (LDH) A and B. These two isoforms share common interacting metabolites including NADH and NAD. However, they also have distinct interaction profiles with two classes of metabolites, adenosine nucleotides and free or acylated CoA species. Using differential scanning fluorimetry (DSF) and enzyme activity assays, Hicks et al. validated that ATP inhibits only LDHA but not LDHB. In parallel, LDHA, but not LDHB, is also inhibited by fatty acid-acyl-CoA. Such differential interaction is also validated at functional level in intact cells. This discovery may have physiological importance as liver expresses mainly LDHA while the heart predominantly expresses LDHB. A differential PMI and metabolite-dependent inhibition for LDH activities may confer fuel utilization priorities between the two organs.4 Lastly, MIDAS uses a defined environment in its assay, while PMIs are likely to be regulated by short or long-term stresses. Diseases like chronic heart failure and metabolic disorders will involve changes of proteins beyond expression levels, particularly in post-translational modifications.5 Despite these limitations, MIDAS may serve as another important tool to provide fundamental insights to decode the complex communications between proteins and metabolites in mammalian system. Like Rosetta stone to connect different languages, future efforts like MIDAS, will one day lead us to fully explore the communication between protein and metabolites in biology and diseases.While this study represents a significant progress made in mapping human proteome interaction with metabolites, MIDAS has several limitations. Firstly, the current platform utilizes a cell-free system to measure PMIs in a defined condition. Therefore, the results may not capture the complexity of PMI in living cells, where sub-cellular compartmentation and protein-protein complexes may significantly alter the PMI profiles. Secondly, the interactions detected by MIDAS cannot infer directly for their functional outcome. The allosteric binding can be inhibitory or stimulative to a targeted protein, affecting its activity, stability, complex formation or intracellular localization. Painstaking efforts are required to further validate the functional outcome of such interactions. Thirdly, MIDAS can profile only relatively stable interactions while PMIs may be further expanded by the fact that many metabolites, including carbohydrates, lipid and amino acids, have dynamic flux in different tissues, requiring time-dependent interactions with targeted proteins.


Drug discovery for a protein target is a laborious and costly process. Deep learning (DL) methods have been applied to drug discovery and successfully generated novel molecular structures, and they can substantially reduce development time and costs. However, most of them rely on prior knowledge, either by drawing on the structure and properties of known molecules to generate similar candidate molecules or extracting information on the binding sites of protein pockets to obtain molecules that can bind to them. In this paper, DeepTarget, an end-to-end DL model, was proposed to generate novel molecules solely relying on the amino acid sequence of the target protein to reduce the heavy reliance on prior knowledge. DeepTarget includes three modules: Amino Acid Sequence Embedding (AASE), Structural Feature Inference (SFI), and Molecule Generation (MG). AASE generates embeddings from the amino acid sequence of the target protein. SFI inferences the potential structural features of the synthesized molecule, and MG seeks to construct the eventual molecule. The validity of the generated molecules was demonstrated by a benchmark platform of molecular generation models. The interaction between the generated molecules and the target proteins was also verified on the basis of two metrics, drug–target affinity and molecular docking. The results of the experiments indicated the efficacy of the model for direct molecule generation solely conditioned on amino acid sequence.The online version contains supplementary material available at 10.1186/s13321-023-00702-2. Despite the compound library contains millions of data, easily scalable compound libraries have covered only a tiny fraction of the synthesized, drug-like chemical space  value estimating how likely a molecule is a viable candidate for a drug. QED is meant to capture the abstract notion of aesthetics in medicinal chemistry.Property distribution provides a visual representation of the properties of the resulting molecular structure.DeepTarget consists of three submodules, namely, Amino Acid Sequence Embedding (AASE), Structural Feature Inference (SFI), and Molecule Generation (MG), corresponding to target sequence embedding module, molecule latent feature inference module, and molecule generation module, respectively. In AASE, it takes the sequence of the protein as input and embeds it to obtain features that represent the protein target. SFI generated molecule feature by applying the architectures of GANs, which exerts the idea of adversarial to force the network to generate results that interact with the protein. For generative models, the sampling distribution of chemical space generated under the same protein as conditions is often highly variable. Therefore, CL is added to the model to further optimize the chemical space and better capture the characteristic similarity of molecules under similar targets. In the end, the molecule representation is decoded to a specific molecule.Whether the generated molecules could bind the target protein is the most important purpose. Two binding sites (PDB ID: 6cm4 and PDB ID: 7kk4) coming from DRD2 (Uniprot ID: P14416)  and PARPDrug–target affinity prediction is a commonly used method to conduct virtual screening. By applying DeepPurpose , all theThe top 1000 molecules of the docking scores were selected from the results of the models with and without the inclusion of individual comparative learning for comparison. Figure As mentioned above, the LBMG has the problem that hard to generate molecules with novel structures. In addition, a representative generated molecule shown in Fig. As mentioned in MOSES , in TablIn addition, a total of 30,000 corresponding generated molecules were randomly selected to calculate QED, LogP, SAscore, NPscore, and MW. The property distribution Fig. b of all Zero-shot generation (no known ligand information in training). Zero-shot ligand generation is very important for real world applications since we cannot always find active off-the-shelf ligands. In this section, we erased all DRD2 and PARP1 data from training set and retrained DeepTarget. After training, DRD2 and PARP1 were input to DeepTarget to generate molecules and 10,000 generated molecules were randomly chosen from each protein for molecule docking. As shown in Fig. In this part, we made some comparisons for DeepTarget with a protein-specific de novo drug generation methods based on sequence-to-sequence model . The training set for DeepTarget were re-trained in Seq2Seq and the two proteins (DRD2 and Parp1) were also applied to take the case study comparison.Because of the complexity and time-consuming of the molecular docking process, we only generated 1000 molecules to calculate the valid, unique and novelty separately and randomly selected 500 to dock for each protein. In , it usedThe comparison of these two methods indicated that both are capable of generating ligand molecules against protein sequences and our model has a greater advantage. On the one hand, in the generated results for untrained protein sequences, our model generates molecules with better interactions. On the other hand, drug design need screening suitable molecules from a large number of candidates, which means not simply generating small batches of molecules. In terms of Seq2Seq, the larger the size of the beam search, the more memory rises sharply and various performance degradation , which iMost DL methods normally involve a known activity library of molecules or bind pockets of a particular protein to produce ligand. In other words, these methods require some prior information about the compounds that are active or complex self-information of the target protein. In general, the methods based on known compounds could not generate novel results. Even some proteins also do not have sufficient known molecules. In addition, the determination of the 3D structure of a protein is not an easy task, and it is quite costly. Conversely, the proposed method does not require in-depth knowledge of any kind of chemical descriptor of the active ligand or molecule nor does rely on information about the 3D structure of the protein. Consequently, the use of amino acid sequences as input to discover molecules could considerably simplify the initial phase of drug discovery.Our study was to generate SMILES molecules simply on the basis of amino acid sequences. The method benefits from the recent progress in the image or text generation field, where this architecture shows state-of-the-art results. First, GAN is an unsupervised learning model whose most important feature is that it provides a method of adversarial training for deep networks. Such method helps better learn the distribution and relationship between data. Second, the products of GANs have greater diversity than machine translation. In addition, the introduction of CL greatly enhances the quality of the generated results, thus better closing the chemical space features of the same protein target. In addition, our methods have rapid process to generate a series of candidate molecules. It has been tested that our model could generate novel molecules at a rate of about 980/s on a single 40G NVIDIA A100 .The above results show that the idea of generating molecules with interrelationships directly from protein target sequences has a certain plausibility. For the generated molecules, the generative effect of the model and the properties of the molecules are similar to the capabilities of the other models. For the target protein, the model could generate molecules that are similar to known active molecules and obtain novel new molecules. Importantly, the affinity scores of the generated molecules are not only related to the distribution of the known active molecules, but their interaction ability could be demonstrated by molecular docking.Additional file 1: Figures and Tables of the comparison method.


Computational approaches for identifying the protein–ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein–ligand binding affinity and achieve significant performance improvement. However, protein–ligand binding affinity prediction still has fundamental challenges. One challenge is that the mutual information between proteins and ligands is hard to capture. Another challenge is how to find and highlight the important atoms of the ligands and residues of the proteins.To solve these limitations, we develop a novel graph neural network strategy with the Vina distance optimization terms (GraphscoreDTA) for predicting protein–ligand binding affinity, which takes the combination of graph neural network, bitransport information mechanism and physics-based distance terms into account for the first time. Unlike other methods, GraphscoreDTA can not only effectively capture the protein–ligand pairs’ mutual information but also highlight the important atoms of the ligands and residues of the proteins. The results show that GraphscoreDTA significantly outperforms existing methods on multiple test sets. Furthermore, the tests of drug–target selectivity on the cyclin-dependent kinase and the homologous protein families demonstrate that GraphscoreDTA is a reliable tool for protein–ligand binding affinity prediction.https://github.com/CSUBioGroup/GraphscoreDTA.The resource codes are available at  Identification of high binding affinity values for candidate small molecules plays an important role in drug discovery and drug design . Considering the interactions between small molecules and proteins, our model also includes a mutual information mechanism to learn and exchange the representations of proteins and small molecules. Unlike existing graph neural networks, GraphscoreDTA mainly has the following novelties: (i) bitransport information mechanism is constructed to bridge the gap between protein and ligand feature extraction; (ii) skip connections are employed in the GNNs to avoid gradient vanish problem; (iii) multihead attention mechanism is designed to weigh the contributions from individual atoms of a ligand and individual residues of a protein, respectively; (iv) gated recurrent units (GRUs) are introduced to dominate the information proportion between the graph general nodes and super nodes; and (v) Vina distance terms are used to optimize the model prediction. To further evaluate the generalization ability of GraphscoreDTA, two variants of the model were trained based on compound and protein similarity splitting strategy, respectively. The results showed that the models have better performances on the test sets compared with the state-of-the-art methods. In particular, we showed that (i) employing 8 Å as the cutoff of protein contact map may help capture both long-range interactions and short-range interactions; (ii) our model can identify important residues and atoms for proteins and ligands, which may reveal the recognition mechanisms among protein–ligand pairs; and (iii) the modeling structures are predictive even when the number of experimentally solved protein structures is insufficient. Furthermore, we showed the potential of GraphscoreDTA for predicting novel inhibitors for disease-related targets.Ki, Kd, and IC50 . In addition, the other two high quality test sets, namely, CASF2016 benchmark  database , and concordance index (CI) are used to measure the predictive performance. As shown in R values of GraphscoreDTA in the CASF2016 test set are 11.5%, 8.6%, 10.2%, 35.1%, and 11.4% larger than DeepDTA, DeepDTAF, Pafnucy, BAPA, and the fusion model. Similar improvement was also observed in the CASF2013 test set. Among remaining metrics, our model achieves best performance of 0.819 (0.039 improvement), 1.249 (reduced by 0.177), 0.981 (reduced by 0.149), and 1.216 (reduced by 0.192) for CI, RMSE, MAE, and SD, respectively. More notably, the SD metric of our model has highest drop with a value of 0.192 than other metrics, while the CI metric has the least improvement (0.039). Furthermore, we also evaluated the model performance based on protein similarity clustering, and found that our model consistently outperformed the other models in all the metrics  may be more effective than model III (protein–ligand interaction GNNs) for affinity prediction. The ablution studies show that combining different configurations, such as the mutual information, individual protein and ligand GNNs, interaction GNNs, and Vina optimization terms, can indeed improve the prediction performance.The ablation studies were conducted to evaluate the effectiveness of main strategies adopted in the modeling . Here, sConsidering the information transfer between different nodes of protein graph, we used a contact map to represent the edges in a protein graph. Here, we calculated the contact maps using different distance cutoffs, then the models with different contact maps were evaluated on the CASF2016 test set. As shown in Here, two representative protein–ligand complexes were selected to visualize the attention contribution of our constructed model. The model can produce multihead attentions by inputting protein contact maps and ligand graphs, where the weights represent the contribution of each amino acid of the protein and each atom of the ligand. Thus, the amino acid/atom with a larger weight value indicates that it is more important for affinity identification. The top-15 weighted residues and atoms were selected to check the overlap with the actual interaction sites . In HIV-Proteins possess specific structures for different folding recognition. In this study, the tertiary structures were needed for identifying the protein–ligand binding affinity which might not be always available. Here, we evaluated the model performance using the predicted structures rather than the experimental structures on the CASF2016 test set. To avoid the sequence error, protein sequences in UniProt database must completely cover the sequence parsed from the PDB dataset. Then AlphaFold2 . In thesR, CI, RMSE, MAE, and SD values are 0.876, 0.704, 0.470, 0.376, and 0.484, respectively. The results suggest that the predicted values are extremely close to the measured values (The cyclin-dependent kinase (CDK) family plays a vital role in the regulation of the eukaryotic cell cycle and has important functions in apoptosis, transcription, and differentiation . Cyclin-d values . For exaFurthermore, we evaluated the performance on the other CDKs including CDK1, CDK2, and CDK8. We collected 5 crystal structures of CDK1 in complexes with compounds, 77 crystal structures of CDK2 in complexes with compounds, and 21 crystal structures of CDK8 in complexes with compounds from the constructed datasets. We calculated the total number of hits among the top 1, top 10, and top 8 predicted candidate compounds for CDK1, CDK2, and CDK8, respectively. More specifically, the top 1 of the known compounds with best measured binding affinity value for CDK1 target was correctly predicted as top ranked by our model . MeanwhiWe applied GraphscoreDTA to the task of drug–target selectivity. Here, the drug–targets with large differences in binding affinity values were considered to evaluate the model performance. For drug catechol, we aim to find the effective targets with the highest pairwise binding affinities . It is kη) with 10 ligands, 3-dehydroquinate-dependent protein kinases-1 (DHQD) with 19 ligands, 3-phosphoinositide-dependent protein kinase-1 (PDPK1) with 25 ligands, acetylcholine receptor (AChR) with 29 ligands, β-glucosidase (GBA3) with 20 ligands, biotin carboxylase (BC) with 10 ligands, protein kinase A (PKA) with 45 ligands, and dual-specificity phosphatase (DSP) with 33 ligands , a potential target, can bind different small molecules and play a relevant role in the treatment of Hepacivirus’s disease. It is interesting to note that the sizes of pocket and small molecules relate to the protein–ligand binding affinity . Therefobtad340_Supplementary_DataClick here for additional data file.


Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.There has been considerable recent progress in designing new proteins using deep-learning methods Fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks yields a generative model for protein design that achieves outstanding performance on a wide range of protein structure and function design challenges. Denoising diffusion probabilistic models (DDPMs), a powerful class of machine learning models recently demonstrated to generate new photorealistic images in response to text prompts15, have several properties well suited to protein design. First, DDPMs generate highly diverse outputs, as they are trained to denoise data  that have been corrupted with Gaussian noise. By learning to stochastically reverse this corruption, diverse outputs closely resembling the training data are generated. Second, DDPMs can be guided at each step of the iterative generation process towards specific design objectives through provision of conditioning information. Third, for almost all protein design applications it is necessary to explicitly model three-dimensional (3D) structures; rotationally equivariant DDPMs can do this in a global representation frame independent manner. Recent work has adapted DDPMs for protein monomer design by conditioning on small protein ‘motifs’9 or on secondary structure and block-adjacency (‘fold’) information8. Although promising, these attempts have shown limited success in generating sequences that fold to the intended structures in silico16, probably due to the limited ability of the denoising networks to generate realistic protein backbones, and have not been tested experimentally.De novo protein design seeks to generate proteins with specified structural and/or functional properties, for example, making a binding interaction with a given target17) (AF2) and RoseTTAFold18 (RF). RF has properties well suited for use in a protein design DDPM . Experimental characterization showed that the method can scaffold a wide range of protein functional motifs with atomic accuracy19, but the approach fails on minimalist site descriptions that do not sufficiently constrain the overall fold and, because it is deterministic, can produce only a limited diversity of designs for a given problem. We reasoned that by fine-tuning RF as the denoising network in a generative diffusion model instead, we could overcome both problems: because the starting point is random noise, each denoising trajectory yields a different solution, and because structure is built up progressively through many denoising iterations, little to no starting structural information should be required. In this study, we used an updated version of RF18 as the basis for the denoising network architecture (17), OmegaFold20, ESMFold21) could in principle be substituted into an analogous DDPM.We reasoned that improved diffusion models for protein design could be developed by taking advantage of the deep understanding of protein structure implicit in powerful structure prediction methods such as AlphaFold2 . To enable RFdiffusion to learn to reverse each step of the noising process, we train the model by minimizing a mean-squared error (m.s.e.) loss between frame predictions and the true protein structure , averaged across all residues  for up to 200 stepsresidues . This loresidues . The m.sresidues .1 to subsequently design sequences encoding these structures, typically sampling eight sequences per design in line with previous work16 , and (2) with self-conditioning25, in which the model can condition on previous predictions between timesteps  of high confidence , less than five), (2) globally within a 2 Å backbone root mean-squared deviation (r.m.s.d.) of the designed structure and (3) within 1 Å backbone r.m.s.d. on any scaffolded functional site (26 and is significantly more stringent than template modelling (TM)-score-based metrics used elsewhere29  in a manner akin to ‘canonical’ diffusion models, with predictions at each timestep independent of predictions at previous timesteps . We experimentally characterized six of the 300 amino acid designs and three of the 200 amino acid designs, and found that they have circular dichroism spectra consistent with the mixed alpha–beta topologies of the designs and are extremely thermostable  Hallucination with RF, an experimentally validated method using Monte Carlo search or gradient descent to identify sequences predicted to fold into stable structures , and thus we further fine-tuned RFdiffusion to condition on secondary structure and/or fold information, enabling rapid and accurate generation of diverse designs with the desired topologies  barrels or cavity-containing NTF2s for small molecule binder and enzyme designies Fig. . In siliies Fig. , and expies Fig. .34, delivery vehicles35 and catalysts36. Cyclic oligomers have been designed using structure prediction networks with an adaptation of Hallucination that searches for sequences predicted to fold to the desired cyclic symmetry, but this approach fails for higher-order dihedral, tetrahedral, octahedral and icosahedral symmetries, probably in part because of the much lower representation of such structures in the PDB7.There is considerable interest in designing symmetric oligomers, which can serve as vaccine platformsn chains, and the monomer chain length, we generate random starting residue frames for a single monomer subunit as in the unconditional generation case, and then generate n − 1 copies of this starting point arranged with the specified point group symmetry. Because RFdiffusion is equivariant (inherited from RF) with respect to rotation and relabelings of chains, symmetry is largely maintained in the denoising predictions; we explicitly resymmetrize at each step but this changes the structures only slightly .Despite not being trained on symmetric inputs, RFdiffusion is able to generate symmetric oligomers with high in silico success rates  that at least 87 had oligomerization states closely consistent with the design models  both during conditional training and inference, and build scaffolds that hold the motif atomic coordinates in place. Many deep-learning methods have been developed recently to address this problem, including RFjoint Inpainting  to a trimerization domain yielded a high-affinity inhibitor that had potent and broadly neutralizing antiviral activity in animal models43. Ideally, however, symmetric fusions to binders would be rigid, so as to reduce the entropic cost of binding while maintaining the avidity benefits from multivalency. We used RFdiffusion to design C3-symmetric trimers that rigidly hold three binding domains  such that they exactly match the ACE2 binding sites on the SARS-CoV-2 spike protein trimer. The designs were confidently predicted by AF2 to both assemble as C3-symmetric oligomers, and to scaffold the AHB2 SARS-CoV-2 binder interface with high accuracy  with ion-specific optimal sidechain–metal bond lengths. RFdiffusion provides a general route to building up symmetric protein assemblies around such sites, with the symmetry of the assembly matching the symmetry of the coordination geometry. As a first test, we sought to design square-planar Ni2+ binding sites. We designed C4 protein assemblies with four central histidine imidazoles arranged in an ideal Ni2+-binding site with square-planar coordination geometry  to which the diffused chain binds Fig. . For des52, Interleukin-7 Receptor-ɑ (IL-7Rɑ)12, Programmed Death-Ligand 1 (PD-L1)12, Insulin Receptor (InsR) and Tropomyosin Receptor Kinase A (TrkA)12. We designed putative binders to each target, both with and without conditioning on compatible fold information, with high in silico success rates E. coli and purified, and binding was assessed through single point biolayer interferometry (BLI) screening at 10 μM binder concentration . Half of our tested unconditional designs express in a soluble way,  and have circular dichroism spectra consistent with the design models and high thermostability. Despite their substantially increased complexity, the ideality and stability of RFdiffusion designs is akin to that of de novo protein designs generated using previous methods such as Rosetta. RFdiffusion enables generation of higher-order architectures with any desired symmetry, unlike Hallucination methods, which have so far been limited to cyclic symmetries. Electron microscopy confirmed that the structures of these oligomers are very similar to the design models, which in many cases show little global similarity to known protein oligomers.RFdiffusion is a comprehensive improvement over current protein design methods. RFdiffusion readily generates diverse unconditional designs up to 600 residues in length that are accurately predicted by AF2, far exceeding the complexity and accuracy achieved by most previous methods , but Hallucination is slow for large systems, Inpainting fails when insufficient starting information is provided and previous diffusion methods had low accuracy. RFdiffusion outperforms these previous methods in the complexity of the motifs that can be scaffolded, the precision with which sidechains are positioned , and the accuracy of motif recapitulation by AF2. The design of MDM2 binding proteins with three orders of magnitude higher affinities than the scaffolded P53 motif demonstrates the robustness of RFdiffusion motif scaffolding. Combining accurate motif scaffolding with the design of symmetric assemblies enabled consistent and atomically precise positioning of sidechains to coordinate Ni2+ ions across diverse tetrameric assembliesThere has been recent progress in scaffolding protein functional motifs using deep-learning methods . A high-resolution cryo-EM structure of one of these designs in complex with influenza HA shows that RFdiffusion can design functional proteins with atomic accuracy. Vázquez Torres et al. demonstrate the ability of RFdiffusion to design picomolar affinity binders to flexible helical peptides54, further highlighting its use for de novo binder design. Vázquez Torres et al. also show how RFdiffusion can be extended for protein model refinement by partial noising and denoising, which enables tuneable sampling around a given input structure. For peptide binder design, this enabled increases in affinity of nearly three orders of magnitude without high-throughput screening.For binder design from target structural information alone, previous work required testing tens of thousands of sequencesThe breadth and complexity of problems solvable with RFdiffusion and the robustness and accuracy of the solutions far exceeds what has been achieved previously. In a manner reminiscent of the generation of images from text prompts, RFdiffusion makes possible, with minimal specialist knowledge, the generation of functional proteins from minimal molecular specifications .55, which should enable RFdiffusion to design nucleic acid binding proteins and perhaps folded RNA structures. Extension of RF to incorporate ligands should similarly enable extension of RFdiffusion to explicitly model ligand atoms, and allow the design of protein–ligand interactions. The ability to customize RFdiffusion to specific design challenges by addition of external potentials and by fine-tuning , along with continued improvements to the underlying methodology, should enable de novo protein design to achieve still higher levels of complexity, to approach and, in some cases, surpass what natural evolution has achieved.The power and scope of RFdiffusion can be extended in several directions. RF has recently been extended to nucleic acids and protein–nucleic acid complexesFurther information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06415-8.Supplementary InformationThe supplementary information file is a single PDF that contains text, figures and tables that aim to help the reader understand the theoretical underpinnings of RFdiffusion, its implementation and its application to the design challenges posed in the paper. Descriptions of in silico and experimental methods can be found within.Reporting Summary


Protein function annotation is fundamental to understanding biological mechanisms. The abundant genome-scale protein–protein interaction (PPI) networks, together with other protein biological attributes, provide rich information for annotating protein functions. As PPI networks and biological attributes describe protein functions from different perspectives, it is highly challenging to cross-fuse them for protein function prediction. Recently, several methods combine the PPI networks and protein attributes via the graph neural networks (GNNs). However, GNNs may inherit or even magnify the bias caused by noisy edges in PPI networks. Besides, GNNs with stacking of many layers may cause the over-smoothing problem of node representations.We develop a novel protein function prediction method, CFAGO, to integrate single-species PPI networks and protein biological attributes via a multi-head attention mechanism. CFAGO is first pre-trained with an encoder–decoder architecture to capture the universal protein representation of the two sources. It is then fine-tuned to learn more effective protein representations for protein function prediction. Benchmark experiments on human and mouse datasets show CFAGO outperforms state-of-the-art single-species network-based methods by at least 7.59%, 6.90%, 11.68% in terms of m-AUPR, M-AUPR, and Fmax, respectively, demonstrating cross-fusion by multi-head attention mechanism can greatly improve the protein function prediction. We further evaluate the quality of captured protein representations in terms of Davies Bouldin Score, whose results show that cross-fused protein representations by multi-head attention mechanism are at least 2.7% better than that of original and concatenated representations. We believe CFAGO is an effective tool for protein function prediction.http://bliulab.net/CFAGO/.The source code of CFAGO and experiments data are available at:  Annotating protein functions is the key for unveiling the mechanism of disease, bringing great benefits for biomedical and pharmaceutical . CurrentIn the past decades, a lot of computational protein function prediction methods have been developed . DependiThere are two main ways to combine protein information from multiple sources. The intuitive and simple way is concatenation, which directly concatenates the representations of multiple sources as the input of classifiers . HoweverIn this study, we propose a new method called CFAGO to cross-fuse single-species PPI network and protein biological attributes via a multi-head attention mechanism. CFAGO contains a pre-training step and a fine-tuning step, and both of them use the multi-head attention mechanism to focus on important information. The pre-training step consists of an autoencoder, which can cross-fuse the effective information while ignoring the noise in the sources. The fine-tuning step learns more distinguishing protein representations for protein function annotation. The experimental results on human and mouse datasets show that CFAGO outperforms state-of-the-art single-species network-based protein function prediction methods, including pure PPI network-based methods and GNN-based fusion methods. Both the ablation study and protein representation visualization show the multi-head attention mechanism has an important contribution to fuse features of PPI network and other sources for single-species protein function prediction.Homo sapiens (human) and Mus musculus (mouse). The PPI data and protein sequence data are retrieved from the STRING (v11.5) database (http://geneontology.org) (version 2022-01-13 release) (We conduct experiments on two species: database . In partrelease) . Proteinrelease) . Specifirelease)  is used.Following the standard CAFA protocol , we extrus study . The staCFAGO introduces multi-head attention layers to cross-fuse protein information from different sources in two steps . The firFor a protein, we use its first-order neighborhood of the PPI network to represent its network structure. Specifically, we first convert the PPI network into a weighted adjacency matrix, in which elements are weights of edges, then normalize elements to range For the protein attributes, we select the widely used protein domain and subcellular location information. Protein attributes are represented as binary vectors by bag-of-words encoding, which assigns 1 to an element in the binary vector if the protein is annotated with the corresponding domain or subcellular location. We filter out protein domain terms that appear less than 6 times in the dataset, following a previous study . WithoutHere we define the multi-head attention layer following a previous study . The mulfined as :where nThe feed-forward network consists of two fully connected layers with a nonlinear activation function :where fThe pre-training step uses an encoder–decoder model to cross-fuse information from two sources. For protein Encoder: the encoder has two parallel multilayer perceptrons (MLPs), each for a source feature, and L multi-head attention layers. As original features of different sources may be sparse and differ in dimension, the original feature vector of protein function , W1∈Rd, e.g. micro-averaged AUPR (m-AUPR) and Macro-averaged AUPR (M-AUPR), F1-score (F1), accuracy (ACC), and F-max score (Fmax). The first three metrics are function-centric measures that evaluate proteins annotated to each GO term, while the last two metrics are protein-centric measures that evaluate GO terms annotated to each protein.The m-AUPR, M-AUPR, F1, and ACC are widely used to evaluate protein function prediction . SpecifiIn addition, we use the Davies Bouldin Score  to evaluWe evaluate CFAGO on the three GO aspects: BPO, MFO, and CCO separately. We use the same empirical hyperparameter set for both species and all of the three GO aspects, and merge the validation and training sets for each aspect to train our model. Specifically, the batch size is 32 for both of pre-training and fine-tuning steps, the encoder MLP hidden dimension size lization . We use lization  as the nlization .Here, we compare CFAGO with eight state-of-the-art PPI network-based methods, including two baseline methods, four network integrate methods, and two GNN-based methods. The baseline methods include the Naïve and BLAST methods of CAFA . The netFor the F1 measurement, CFAGO also outperforms the competing methods, only except on mouse MFO where BLAST and Graph2GO achieve better performance. This is not surprising, because several studies have pointed out that MFO is highly correlated with sequence information , and GraWe further investigate the AUPR performance of CFAGO in individual GO terms. The results in Such outstanding results of CFAGO demonstrate the feature cross-fusion via multi-head attention mechanism has obvious advantages compared with pure PPI network-based methods and the multi-source combining method based on GNN.To quantitatively evaluate the effectiveness of cross-fusion by attention mechanism and pre-training, we compare the distinguishing power of protein representations by Davies Bouldin Score .We compare four types of protein representations, including the original PPI network representation, original attribute representation, and hidden embedding representations learned by CFAGO with and without attention mechanism. The structure of CFAGO without multi-head attention mechanism is shown in In addition, we visualize the distribution of above protein representations on human and mouse datasets for the three aspects of GO via t-Distributed Stochastic Neighbor Embedding  by assigThe representation learned by CFAGO without cross-fusion mixes up the shape of distribution of original PPI network structure and original attribute, showing an edge that is similar to the ring part in the distribution of original attribute. The hidden embedding representation learned by full CFAGO model shows a much better cluster segmentation than compared representations. These results show that the features from two sources are indeed cross-fused by multi-head attention mechanism.Here we conduct ablation experiments to study the contribution of self-supervised pre-training and attention mechanism to performance improvement . SupplemWe observe that the performance drops significantly if not applying self-supervised pre-training. The reason is that the CFAGO model contains a huge amount of parameters. As there are only several thousands of training proteins, the CFAGO model is over-fitted without applying pre-training. For the multi-head attention mechanism, the performance of CFAGO drops clearly without it, except on Fmax measure of mouse MFO. The reason is that there are only 1185 mouse MFO training proteins, therefore adding the attention mechanism makes the CFAGO model overfitted, leading the performance drops down. Overall, these results also demonstrate the feature cross-fusion via multi-head attention mechanism has obvious advantages compared with concatenation of features.Here, we analyze the contribution of different features for the three GO aspects. We test five feature conditions: protein domain feature only, protein subcellular location feature only, concatenation of protein domain and subcellular location features, PPI network structure feature only, and combination of all features. The reduced performance in terms of Fmax on mouse MFO is caused by the noise in protein attribute data, and insufficient number of training proteins. Protein domains came from the pfam  databaseIn this study, we propose CFAGO, an attention mechanism-based neural network model, for protein function prediction. It cross-fuses the information from multiple sources of single species using an attention mechanism to learn more effective protein representations. Specifically, CFAGO is the first pre-trained via an encoder–decoder architecture to learn the universal protein representations and then fine-tuned to further improve protein function prediction. We show that CFAGO outperforms state-of-the-art single-species network-based protein function prediction methods in both human and mouse organisms. CFAGO would be an effective tool for understanding disease mechanisms or finding drug targets.Several studies  have shobtad123_Supplementary_DataClick here for additional data file.


Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand.The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset.https://github.com/abbiAR/PLBAffinity.Code and data uploaded to  The early stages of drug development rely on finding promising lead compounds. Computational approaches are increasingly utilized to identify such compounds, and molecular docking is a widely used tool for this purpose. Molecular docking deploys different algorithms to generate potential ligand binding poses and resultant affinity estimations within a target binding site . These aPrevious studies have made use of the spatial relationship between the ligand and protein constituents in their lowest energy conformation ,b. CurreThese results led us to ask what we could achieve using nonspatial descriptors. Previous studies have highlighted the benefits of adding basic ligand descriptors when predicting binding affinity from a protein–ligand complex , these features are listed in Kd/Ki) activity values provided in the index files that accompany the CASF datasets.For each protein structure, a count of the amino acids featured in its active site was performed. The authors of the CASF datasets provide pdb files for all their binding sites, these files include amino acids within 10 Å of the bound ligands. For every protein, each amino acid type was encoded into a vector with the number of occurrences as its corresponding value. Only standard amino acids were considered generating a 20-unit amino acid vector. In addition, eighteen ligand properties were calculated using RDKit  .https://www.r-project.org). The hyperparameters were optimized using a grid search, and kept all but the number of rounds, max depth, and learning rate as default values, where number of rounds = , max depth = , and learning rate = . We used internal 5-fold CV on only the training sets to identify the best hyperparameter combination. We report the mean Pearson’s correlation coefficient (R), along with the root mean squared error (RMSE) from 5-fold CV. It is worth noting here that this 5-fold CV is different from the internal CV used on the training sets to determine the best hyperparameters.We performed our experiments using the machine learning method extreme gradient boosted trees in R (Ki/Kd) was set as the target value and hence, only samples with an associated Ki/Kd value was included. The resultant dataset consisted of 63 281 compatible ligands across 325 unique proteins. Machine learning was applied as described for the previous experiments. Predictions were performed using the ligand properties, the amino acid counts as well as the two features combined. As the number of drugs per protein is heavily skewed towards a few targets, different thresholds on maximum number of ligands for including targets in the datasets were applied . Datasets and results are available in a GitHub repository (https://github.com/abbiAR/PLBAffinity).ChEMBL experiments: amino acid counts were performed for all protein pockets and chains in CASF v.2019 as described above. If a protein had more than one structure present in the dataset, the mean count for these entries was used. Proteins from CASF v.2019 were cross referenced with the ChEMBL database . Ligand properties for RDKit-compatible small molecules (<700 kDa) were calculated using RDKit. Where more than two entries for the same protein–ligand pair were present, only the last entry was kept. −log  outperforms all but one of the state-of-the-art approaches  is leveraged in the learning cf.  and 5. TThe fact that our suggested descriptors can be leveraged to such an extent across the CASF datasets does suggest that there is a critical need to design new datasets for the task of predicting protein–ligand complex affinity using machine learning. Such datasets should ensure that predictions are based on correlations between specific interactions between protein and ligand within the binding site. Other studies have also observed the tendency of machine learning models to perform well using nonspatial features when trained on the CASF datasets (Future studies employing our proposed approach may adopt more robust datasets derived from ChEMBL and other sources with regards to inclusion criteria of proteins and ligands to reduce any potential bias. However, this study simply set out to demonstrate that the combination of ligand properties and an amino acid count improves affinity prediction for protein–ligand complexes. The attraction of this approach is its simplicity. The predictions presented herein are likely to improve further by the addition of nontrivial information about ligand, protein, or their interaction.btad502_Supplementary_DataClick here for additional data file.


Hennekam Lymphangiectasia–Lymphedema Syndrome 3 (HKLLS3) is a rare genetical disorder caused by mutations in a few genes including ADAMTS3. It is characterized by lymphatic dysplasia, intestinal lymphangiectasia, severe lymphedema and distinctive facial appearance. Up till now, no extensive studies have been conducted to elucidate the mechanism of the disease caused by various mutations. As a preliminary investigation of HKLLS3, we sorted out the most deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) that might affect the structure and function of ADAMTS3 protein by using a variety of in silico tools. A total of 919 nsSNPs in the ADAMTS3 gene were identified. 50 nsSNPs were predicted to be deleterious by multiple computational tools. 5 nsSNPs  were found to be the most dangerous and can be associated with the disease as predicted by different bioinformatics tools. Modelling of the protein shows it can be divided into segments 1, 2 and 3, which are connected by short loops. Segment 3 mainly consists of loops without substantial secondary structures. With prediction tools and molecular dynamics simulation, some SNPs were found to significantly destabilize the protein structure and disrupt the secondary structures, especially in segment 2. The deleterious effects of mutations in segment 1 are possibly not from destabilization but from other factors such as the change in phosphorylation as suggested by post-translational modification (PTM) studies. This is the first-ever study of ADAMTS3 gene polymorphism, and the predicted nsSNPs in ADAMST3, some of which have not been reported yet in patients, will serve for diagnostic purposes and further therapeutic implications in Hennekam syndrome, contributing to better diagnosis and treatment.The online version contains supplementary material available at 10.1186/s12859-023-05361-6. Hennekam syndrome is a rare autosomal recessive disorder that manifests with abnormalities in the lymphatic system. It was first described by Dutch physician Hennekam in 1989 , it reprApproximately 25% of individuals with Hennekam syndrome display homozygous variations in the CCBE1 gene  . CCBE1 iFurthermore, it has been established that ADAMTS3, which is predominantly expressed in cartilage, is involved in skin, lung, and aorta pathologies, as well as being colocalized with type II pro-collagen, a crucial cartilage component, and the nervous system. ADAMTS3 is also more abundant in human skin and skin fibroblasts compared to ADAMTS2 . ProcollHence, the identification of mutated genes and their functions has become the focus of our attention. Single nucleotide polymorphisms (SNPs) occur every 200–300 base pairs in the human genome, serving as genetic markers . Approxihttp://www.ncbi.nlm.nih.gov/) and the Ensembl genome browser (http://asia.ensembl.org/Homo_sapiens/Gene/Summary). Figure The SNPs of the ADAMTS3 gene were examined using the NCBI database  to determine their potential impacts on protein structure and function. Among the 919 nsSNPs, SIFT and PolyPhen-2 predicted 45 (forty-five) to be deleterious or damaging, as shown in Table p value of 0.001. The results of mutation prediction tools other than SIFT and PolyPhen-2 and the significance of all other tools are presented in Additional file Various computational algorithms were employed to investigate the pathogenic implications of the 919 nsSNPs in the ADAMTS3 gene. Out of these, only 50 nsSNPs were identified as completely deleterious by the SIFT tool and subjected to further analysis by several other tools. The top 5 nsSNPs were determined to be deleterious by all algorithmic tools. However, the number of deleterious SNPs varied among the different computational tools. For instance, PolyPhen-2 predicted 45 deleterious nsSNPs but did not validate any of the five nsSNPs that SIFT classified as deleterious with a cutoff of > 0.5. The FATHMM tool identified only 10 nsSNPs (20%) out of 50 SIFT-predicted nsSNPs as deleterious, while SNAP2 predicted 45 nsSNPs (90%) as harmful, and PROVEAN predicted 45 nsSNPs (90%) to be severely detrimental. The PANTHER algorithm predicted the deleterious impact of 46 (92%) nsSNPs, with 40 nsSNPs classified as probably damaging, 6 nsSNPs as possibly damaging, and 4 as probably benign on the ADAMTS3 protein. Mutation Assessor identified 12 nsSNPs as deleterious, while P-Mut predicted 25 (65.21%) to be deleterious. LRT predicted 44 (88%) nsSNPs to be deleterious, while PhD-SNP  and PON-MutPred2 is a computational tool that evaluates the molecular properties of amino acid residues in humans to determine whether a substitution is associated with the disease. The score generated by MutPred2 indicates the likelihood of an amino acid substitution affecting the protein's function. A score threshold of 0.5 is used to predict pathogenicity, with scores greater than or equal to 0.8 considered highly confident . The higBased on the ConSurf analysis, it was found that 50 missense deleterious SNPs were distributed across various locations   are confirmed by multiple software as deleterious (C567Y is not included because it occupies the same position as C567R), while 4 of them  are discovered clinically. The wildtype and mutant models are validated by Ramachandran plot and MolProbity all-atom contact analysis (see Methods). The wildtype model shows 1032 residues (85.8%) in the favored region, 77 (6.4%) in the allowed region and 94 (7.8%) in the outlier region, and the total residues in the favored and the allowed regions are 1109 (92.2%). The mutant model shows 1008 residues (83.8%) in the favored region, 110 (9.1%) in the allowed region and 85 (7.1%) in the outlier region. and the total residues in the favored and the allowed regions are 1118 (92.9%).  in ADAMTS3 on protein stability was evaluated using MUpro and INPS3D  and the COACH ligand-binding site prediction server. The RaptorX Binding analysis identified a pocket multiplicity of 151, which was the highest observed, and was linked to the residues G365, M366, Q367, G368, Y369, V395, H398, E399, H402, H408, A426, P427, L428, and V429, with an expected ZnWe study the effects of each mutation and how they affect the structures nearby. The impact of 50 pathogenic ADAMTS3 non-synonymous single nucleotide polymorphisms (nsSNPs) on amino acid sizes, charges and hydrophobicity is analyzed by Project HOPE. Among these nsSNPs, 26 caused a decrease in amino acid size, whereas 22 led to an increase. At 23 sites, the charge was altered, with 20 changing from positive to neutral, one changing from neutral to positive, and two changing from negative to neutral. Hydrophobicity was decreased in seven mutations, while 22 others resulted in an increase. These findings suggest that alterations in amino acid properties at these positions have the potential to impact protein structure and its interactions with other molecules, ultimately affecting protein function  including methylation, phosphorylation, ubiquitination and glycosylation for the wildtype and mutant structure. GPS-MSP predicted there are no methylated sites in ADAMTS3. The predicted phosphorylation sites at serine, threonine and tyrosine by various kinases differ between NetPhos 3.1 and GPS 6.0, with GPS 6.0 predicting more phosphorylation sites than NetPhos 3.1 for both structures  is also conducted. The two most crucial indicators for determining a macromolecule’s structural activity are Rg determination and calculations of distance. The rate at which a protein folds is proportional to its compactness and can be measured using a sophisticated computer method for calculating the gyration radius. From Rg (radius of gyration) analysis of wildtype and mutant structures, it can be observed that the mutant one revealed overall higher Rg values over the simulation time scale when compared to those of wildtype in both segments 1 and 2, but the difference for segment 1 is not as significant as that for segment 2. As a result, the flexibility of the mutant is increased Fig. A, B.Fig.Solvent accessible surface area (SASA) analysis indicated that the mutant structure has a higher SASA value than wildtype for both segments 1 and 2 Fig. A, B. SinThe total number of H-bonds within the proteins was also calculated during the MD simulation as depicted in Fig. In this study, PCA was used to analyze the trajectories and structures of the wildtype and mutant proteins in segments 1 and 2. The plots generated from PCA analysis show the collective motions of the system projected onto the first two principal components.The plots of segments 1 and 2 Fig. A, B indiFEL plots are made after the PCA analysis using the first two principal components. In the plots of FEL Fig. A–D, the in-silico techniques. Specifically, 848 nsSNPs of the ADAMTS3 gene were identified in the dbSNP database, 919 in the Ensembl database, and only 16 of the known MAFs of nsSNPs in the ADAMTS3 gene were less than 1%. SIFT and PolyPhen-2 confirmed that 50 nsSNPs in the ADAMTS3 gene are high-risk and deleterious.The lymphatic system plays a crucial role in maintaining the fluid and tissue balance of the body. The transcription factors PROX1 and SOX18n collaborate to induce the growth and differentiation of lymphatic endothelial cells from veins. Primary lymphedema, which can be solitary or syndromic, may result from the malfunction of several other proteins that are essential for lymphatic system development. To date, 27 genes have been associated with primary lymphedema . The preThe top five nsSNPs with high risk are highly deleterious, as indicated by all cutting-edge prediction techniques utilized in this study  on sequence conservation, sequence features, and structural factors . We usedNumerous biological procedures and processes depend on amino acids, especially highly modified and conserved protein interactions. SNPs in conserved loci can cause more significant deleterious effects than those in non-conserved loci . In our Protein stability is a critical factor for the biological function, activity, and control of biomolecules. Pathogenic missense mutations often result from improper folding and reduced stability of proteins , 33. ProExperiments are considered more reliable in classifying pathogenic mutations. However, conducting recurrent experiments on all non-synonymous single nucleotide polymorphisms (nsSNPs) is time-consuming. Therefore, the methods used in this analysis support the various outcomes of SNPs that illustrate pathogenicity. The importance of deleterious nsSNPs that affect a potential phosphorylation site was highlighted by post-translational modification (PTM) prediction. Modeling of the wildtype and mutant proteins is a reasonable assumption, but it should be noted that even small changes in one segment can potentially affect the overall stability and conformation of the protein, ultimately affecting its function. Hence, it is crucial to analyze each segment separately to understand the potential impact of the mutations. PTM prediction is relevant in the context of nsSNPs as PTMs can be affected by changes in the amino acid sequence resulting from nsSNPs. PTMs are chemical modifications that occur after protein translation and can alter protein function, localization, stability, and interactions with other molecules. If an nsSNP occurs within a PTM site, it may affect the likelihood or efficiency of PTM occurrence, which can in turn impact protein function. For instance, if an nsSNP causes a change in the amino acid sequence at a phosphorylation site, it may alter the ability of kinases to recognize and phosphorylate the site, affecting downstream signaling pathways. PTM prediction tools can help identify potential PTM sites within a protein sequence and assess the impact of nsSNPs on these sites. This information can be useful in understanding the potential functional consequences of nsSNPs and prioritizing nsSNPs for further investigation.The MD simulation studies were introduced to explore the dynamic behavior and stability of the protein. The RMSD, RMSF, secondary structure, Rg, SASA and hydrogen bonding analyses suggest that the mutations have different effects on the stability of the two segments of the ADAMTS3 protein. After including the 25 mutations, segment 2 shows a more significant destabilization effect than segment 1. The analysis of secondary structure shows that in segment 1, the mutations do not significantly affect the stability of the structure, but they may alter the distribution of secondary structures. In segment 2, the mutations lead to destabilization of the structure, particularly in the region of residues Met478-Pro523, which show higher RMSFs and disruption of β-sheets. This secondary structure analysis also shows the remote effect in Project HOPE analysis—the secondary structures next to most of the mutation sites are not disrupted, but the disturbances occur at another part within the segment, again suggesting that the effects of mutations act via tertiary interactions. Additionally, the mutant structure exhibits a higher RMSD, RMSF, Rg and SASA compared to the wildtype for segment 2, indicating greater flexibility and suggesting that the mutations may affect the overall compactness and shape of the protein. The decrease in the number of H-bonds in the mutant structure compared to the wildtype further supports the destabilizing effect of the mutations, which aligns with the fact that β-strands are disrupted in segment 2. These findings could provide insights into the functional consequences of the mutations and guide further experimental investigations. However, the effect of the mutations on segment 1 is relatively small, with little difference in RMSD, RMSF, Rg and the number of H-bonds observed between the wildtype and mutant structures, which might be explained by the fact that the mutations increased the percentage of α-helices when there is a decrease in β-strand percentage. This suggests that the main stabilization effects of the mutations come from segment 2. Since we did not include C567Y in our protein model as it is at the same position as another mutation C567R, the effects of C567Y have not been extensively studied in MD simulation. Further studies are needed to show how it affects the protein.It is noted that among the 25 mutations included in modeling the mutant structure, 4 of them, namely I291T, A370T, Y536C and C567R, are predicted to affect the stability of the protein by MUpro and INPS3D. 2 of them (I291T and A370T) are in segment 1 while the other 2 (Y536C and C567R) are in segment 2. Despite the 2 destabilizing mutations in segment 1, it is found with MD simulation that segment 1 does not have significant destabilization effects when they exist in the model. Since MUpro and INPS3D predict the effects of each mutation individually without considering other mutations, while MD simulation uses a mutant model that incorporates multiple mutations at the same time to study their effects, this suggests that the combined effects of multiple mutations are not just a simple superposition of individual mutations, which is indicated by the insignificant destabilization in segment 1 although the 2 destabilizing mutations (I291T and A370T) are included. When both destabilizing mutations, together with other mutations, exist, the destabilizing effects were alleviated. This might be evidence that compensatory effects of mutations are present in ADAMTS3. Similar intragenic compensatory mutations were also observed in various organisms and viruses such as DNA Bacteriophage φX174 . It is wThe fact that the stability of segment 1 is not destabilized, but the mutations are still deleterious might be rationalized by the results in the PTM analysis. We showed that there are a lot of changes in phosphorylation sites in segment 1. Since phosphorylation is a crucial mechanism for the activation and deactivation of enzymes in organisms , the mutPrincipal component analysis indicates a difference in the trajectories of the two segments after incorporating the mutations. FEL plots of the two segments of the wildtype and mutant structures further show that the stable conformations and Gibbs free energy change are not the same, which might change the shape in its stable form, preventing the protein from working properly.In silico studies also have limitations, the first of which is that the reported causative nsSNPs were only partial, necessitating laboratory confirmation to confirm the findings. Our results provide data on the ADAMTS3 gene's pathogenic nsSNPs, protein 3D structure, potential PTMs sites, and gene–gene interaction, which could aid future research into the gene's function in Hennekam syndrome and other related disorders.We identified 22 deleterious mutations, 5 of which  are exceptionally deleterious. We also included 4 other mutations previously discovered clinically. They have various effects on post-translational modification and ligand binding. Some of them were also shown to destabilize ADAMTS3. Further molecular dynamics simulation by modeling with 21 deleterious mutations and 4 clinically discovered mutations suggests that these mutations have various effects on different parts of the protein. These studies can help future research on the important SNPs for causing the disease. The association between the predicted deleterious SNPs and HKLLS3 can be tested by in vivo studies in animal models and clinical studies in a large population.https://www.ncbi.nlm.nih.gov/snp). Specifically, missense SNPs were collected by selecting them from the list and downloading the resulting list of missense SNPs in the ADAMTS3 gene by clicking “Send to” and then “File”. From this list, only the Reference SNP cluster ID [The Human SNP information for the ADAMTS3 gene was compiled using multiple online sources, including Mendelian Inheritance in Man (OMIM) , NCBI dbuster ID  was useduster ID . The assThe nature of non-synonymous single nucleotide polymorphisms (nsSNPs), whether they are deleterious/damaging or tolerated, was determined in this study using two methodologies, namely SIFT and PolyPhen-2. SIFT predicts harmful nsSNPs by analyzing protein homology sequence and natural nsSNP alignments. When the score is less than 0.05, SIFT considers the effect of nsSNPs on protein function to be deleterious . On the The results obtained from the SIFT and PolyPhen-2 methodologies were further validated by other computational algorithms, including Mutation Assessor, PON-P2, SNPs&GO, PROVEAN, PMut, SNAP2, PANTHER, VarCards, and PhD-SNP, to confirm the functional consequences of missense mutations. The PROVEAN algorithm was used to calculate the detrimental effects of nsSNPs on ADAMTS3 protein sequences. In cases of homologous sequence, delta alignment scores were used by these tools to compare protein sequence and variant versions. An alignment score of ≤ -2.5 indicated a harmful nsSNP . The SNAhttp://pantherdb.org/apparatuses/csnpScoreForm.jsp) uses homologous proteins to identify possible ancestral protein sequences at nodes of phylogenetic trees using an evolutionary preservation measure [PMut is a neural network-based algorithm that accurately predicts whether a single amino acid point mutation will be pathogenic with an 80% success rate in humans. The PMut website can distinguish between neutral variations and disease-associated protein sequences when a FASTA sequence is uploaded. A score greater than 0.5 indicates the presence of pathogenic nsSNPs . SNPs&GO measure . The ori measure .http://mutpred.mutdb.org/) utilizes multiple aspects of protein structure, function, and evolution [In order to predict the molecular mechanism underlying a disease caused by an amino acid substitution in a mutated protein, the MutPred web server (volution . To predhttps://genemania.org/) was employed [In order to investigate the association between the ADAMTS3 gene and other genes based on pathways, expression, localization, genetics, and protein interaction, GeneMANIA (employed .https://genemania.org/) was utilized [To confirm the relationship between the ADAMTS3 gene and other genes based on pathways, expression, localization, genetics and protein interactions, GeneMANIA  was used for this purpose. This tool takes into account various aspects, such as hydrophobicities and molecular weights of the original and mutated amino acids, the difference in alignment scores, the mutation probability of the original residue, the relative solvent accessibility (RSA) of the native amino acid, and the local energy difference between the wildtype and mutant protein structures. To predict the stability changes caused by each mutation, the PDB file of the AlphaFold-predicted structure of ADAMTS3 and the mutations predicted by SIFT and PolyPhen-2 were input into INPS3D. The change in stability caused by each mutation was measured by ΔΔG [INPS3D, a newly developed tool, is capable of predicting protein stabilities in wildtype and mutant. The INPS-MD (Impact of Non-synonymous mutations on Protein Stability—Multi Dimension) web server  is a computational tool that predicts protein binding sites based on a 3D model and other features including the secondary and tertiary structure of the protein, disordered regions, solvent accessibility, and others [p value and a pocket multiplicity factor when predicting binding sites. A higher score indicates a higher degree of reliability for the predicted binding pocket, specifically when the score exceeds 40.RaptorX Binding (d others . In addihttp://zhanglab.ccmb.med.umich.edu/COACH/) utilizes two comparison approaches, TM-SITE and S-SITE, to recognize ligand binding templates from the BioLiP protein function database, as well as binding-specific sub-structure and sequence feature correlations. The COACH server ranks the top ten models based on cluster size, PDB hits, ligand names, consensus binding residues, and readily available, downloadable complex structures, assigning a C-Score to each model. The C-score is a measure of the predicted reliability and ranges from 0–1, with higher scores indicating greater reliability [To predict protein–ligand binding sites, the meta-server program COACH  [https://gps.biocuckoo.cn/online.php) and NetPhos 3.1  [http://www.ubpred.org), and BDM-PUB (http://bdmpub.biocuckoo.org/) [https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/), which predicts glycosylation sites based on a threshold value of 0.5 for the predicted glycosylation score [In order to predict protein activity based on post-translational modifications (PTMs), we employed various tools to predict potential PTM sites in ADAMTS3 protein. To predict methylation sites, we used GPS-MSP 1.0 (ine.php) . For theos-3.1/) , 59. Netoo.org/) , 50. To on score , 61.An accurate tool for predicting protein structure, AlphaFold 2 , was utiIn this study, we utilized a web-based program called Project HOPE to assess the impact of amino acid mutations on their physical properties, including size, charge, and hydrophobicity . The strFor the study, molecular dynamics simulations were carried out for 200 ns using the Desmond algorithm from Schrodinger LLC , 67. TheIn order to generate the 4 PDB files of average wildtype and mutant structures in 2 segments of the frames after 170 ns, we utilized GROMACS and the gmx covar command with the -av parameter . SubsequPrincipal component analysis (PCA) is a widely used method for analyzing molecular dynamics (MD) trajectories. This method involves mapping the coordinates of each atom in each frame of the trajectory onto a set of orthogonal vectors  in order to reduce the dimensionality of the data. The covariance matrix of the atomic coordinates is calculated, and the eigenvalues and eigenvectors are obtained through diagonalization of the covariance matrix, generating the principal components.In this study, GROMACS software was used to perform PCA. Two commands, gmx covar and gmx anaeig, were utilized for this purpose. Only the coordinates of the C-alpha atoms were considered in the analysis.The free energy landscape (FEL) is a tool for exploring the energy distribution of a protein in various variables, including principal components, and provides insight into its stability and conformational changes. The Gibbs free energy, which incorporates both enthalpy and entropy, is calculated at each state to generate the FEL.In our study, FEL analysis was performed using GROMACS with the gmx sham command. The Gibbs free energy was calculated as a function of the first two principal components, considering only the C-alpha atoms.p values ≤ 0.001 were considered significant.Using SPSS version 23 and Microsoft Excel, correlation analysis was performed on computational in silico tool predictions. The Student’s t-test was used to investigate significant differences predicted by the numerous computational algorithms. Additional file 1: Figure S1. Overall significance of the prediction tools used in the study. Table S1. Confirmation of the deleterious nsSNPs by other prediction software.Additional file 2: File S2. Results of MutPred2 analysis.Additional file 3: File S3. Consurf analysis.Additional file 4: File S4. Ramachandran Plot for wildtype and mutant model.Additional file 5: File S5. Output from MolProbity all-atom contact analysis.Additional file 6: File S6. The effects of 50 high-risk nsSNPs of ADAMTS3 gene on protein stability predicted by MUpro, INPS3D and MAF.Additional file 7: File S7. Predicted effects of the mutations from 50 high-risk pathogenic nsSNPs of ADAMTS3 on amino acid size, charge and hydrophobicity.Additional file 8: File S8. Changes in 3D protein structures and amino acid structures of important mutations.Additional file 9: File S9.—Phosphorylation analysis of mutant protein by GPS 6.0.Additional file 10: File 10.—Ubiquitylation analysis of mutant protein by BDM-PUB.Additional file 11: File 11.—O-glycosylation analysis of mutant protein by NetOGlyc 4.0.


Beta arrestins are a family of adaptor proteins that help in the regulation of signaling and trafficking of various G protein coupled receptors (GPCRs). Six oxadiazole derivatives taken from literature are analyzed for anti-cancer properties. Thetoxicity profiles of all the drugs were similar to Tamoxifen used as control. Data shows that compounds 2, 4, and 6 exhibited comparably significant molecular interactions with the cancerous protein for further consideration. Beta arrestins are cytoplasmic proteins which showcase maximum expression throughout the human body. They regulate the G protein coupled receptors and come out as important nodes in cellular signaling pathways . Betaar2D structures (mol.) of oxadiazole compounds (1-6) were drawn using the ChemDraw 16.0 software . During The 3D crystal structure  of the rA graphical user interface program called as Auto Dock 4.2.6 was utilized for setting the grid box and subsequently do the docking simulations. Several, different docking pockets and poses were analyzed, but finally the grid was generated with best 9conformational poses, these structures had the most favorable (least) free binding energy, leading to significant interactions between the ligand and the prepared optimized protein.These predictions help to understand the drug efficiency and provide insight over the studied ligand properties, analyzing if it is an orally active drug or not. The prediction is done on the basis of Lipinski's rule of five. The chemical structures of thecompounds (1-6) were converted to their canonically simplified molecular structures. SwissADME tool was used for the estimation of the pharmacokinetic parameters. The ligands were subjected to the Lipinski's screening using the SwissADME and pre ADMET predictor.Organ toxicities and toxicological endpoints of the ligands and their LD50 values were predicted using ProTox II online server.One way ANOVA was used for statistical analysis. The clinically proven drugs are used as a control and the results are compared. The significance of the results was found to be p< 0.05 The 3D structures of the original and prepared protein show structural conformational differences. The 2D structures of all the ligands are also provided with the similar oxygen , sulfur and nitrogen groups present. The different interactions of the Van derwaals , conventional hydrogen bonds , Pi sulfur , alkyl and Pi alkyl bonds are observed  in all tThe oxadiazole derivative ligands, acted as CY2D inhibitors and also obeyed the Lipinski's rule of five, the compounds had low GI absorption and had presented no blood brain barrier permeation . The ligThe PROTOX II value signified the toxicity of all the drugs taken under consideration, observation was that only compound 1 had inactive carcinogenicity, other ligands exhibited active carcinogenicity . LD50vaTo study the interaction and binding affinity between the beta arrestin-1 protein and oxadiazole compounds in a 3D fashion, compounds were docked within the binding sites of the protein . For theWe document the molecular docking analysis of the tumor protein beta arrestin-1 with oxadiazole compounds. Data shows that compounds 2, 4, and 6 exhibited comparably significant molecular interactions with the cancerous protein for further consideration.