However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein–protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein–protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.Physical interactions between proteins are essential for most biological processes governing life A surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling the de novo design of protein interactions and of artificial proteins with function. Robust computational methods to design de novo PPIs could be used to rapidly engineer protein-based therapeutics such as antibodies and protein inhibitors or vaccines among others, and are therefore of considerable interest for biomedical and translational applications8.Designing novel protein–protein interactions (PPIs) remains a fundamental challenge in computational protein design, with broad basic and translational applications in biology. The challenge consists of generating amino acid sequences that engage a target site and form a quaternary complex with a given protein. This represents a stringent test of our understanding of the physicochemical determinants that drive biomolecular interactions8 and prediction12, designing novel protein binders against specific targets is very challenging, particularly when no structural elements from pre-existing binders are known. Current state-of-the-art methods for de novo PPI design14, such as hotspot-centric approaches6 and rotamer information fields8, rely on placing disembodied residues on the target interface and then optimizing their presentation on a protein scaffold. Intrinsic limitations of these approaches relate to the very weak energetic signatures provided by scoring functions to single-side chain placements, which is compounded in flat interfaces that lack deep pockets. These methods also face the challenge of finding compatible protein scaffolds to precisely display the generated constellations of residues. To circumvent these limitations, new approaches are needed to design de novo binders to various surface types and protein sites.Despite recent advances in rational PPI design16. The complementarity features arise as a consequence of the energetic contributions that are critical to stabilize PPIs, including van der Waals interactions (geometric complementarity), hydrophobic effect and electrostatics interactions 15. At the structural level, most protein interfaces contain surface regions that become inaccessible to solvent after complex formation, which we refer to as buried or the core interface, as well as patches that are involved in the interface but remain solvent-exposed, which we refer to as the interface rim. Residues within the buried areas tend to be much less tolerant to mutations17 and have a large energetic contribution towards the PPI formation, often referred to as hotspots. Rim regions are generally more polar and tolerant to mutations, giving important contributions to affinity and, notably, specificity18. Guided by these general principles of molecular recognition, we introduce a protein design approach based on the critical importance of the fully buried patches of the interface to drive protein interactions. We implemented these design principles by taking advantage of surface fingerprints learned from interacting protein surfaces that capture features that are determinants for molecular recognition. Our approach enables ultrafast and accurate prediction of privileged sites for PPI design, and reduces the complexity for hotspot search and grafting. We used this design workflow to successfully engineer and characterize binders against four therapeutic targets of interest—namely, SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4.A long-standing model of molecular recognition postulates that PPIs form between protein molecular surfaces with chemical and geometric complementarity10. Within this framework we developed the MaSIF-site tool to predict areas with a propensity to form PPIs on the surface of proteins. MaSIF-site receives as input a protein decomposed into patches and outputs a per-vertex regression score on the propensity of each surface point to become a buried site within a PPI. We also developed MaSIF-search, another tool to evaluate the surface complementarity between binding partners. MaSIF-search was designed as a Siamese neural network architecture19 trained to produce similar fingerprints for the target patch versus the binder patch, and dissimilar fingerprints for the target patch versus the random patch. As MaSIF tools had robust performance in PPI-related prediction tasks, we hypothesized that we could use them to design PPIs by targeting sites using only structural information from the target protein. To address the de novo PPI design problem, we devised a three-stage computational approach depicted in Fig. 20.In previous work, we introduced a geometric deep-learning framework—Molecular Surface Interaction Fingerprinting (MaSIF)—to generate surface fingerprints from the geometric and chemical features of molecular surfaces and learn patterns that determine the propensity of protein interactions21, uncoordinated water molecules in the interface or incompatible charges—are sufficient to disrupt PPIs22.The new MaSIF-seed protocol addresses the problem of identifying binding seeds that can mediate productive binding interactions Fig. . This ta2 of surface area, consistent with the buried surface areas observed in native interfaces  identified only 6 out of 31 as top results in the helical set, 21 out of 83 in the non-helical set. In addition to superior performances, MaSIF-seed was considerably faster, showing speed increases of between 20- and 200-fold, which mostly depend on the number of patches derived from each motif. In our benchmark, we also performed comparisons with faster methods, which showed much lower performances than ZDock + ZRank2  < 3 Å) among 1,000 decoys  the interaction site could be correctly identified by the method, and (2) the majority of contacts lie on a radial patch at the interface core, and with a high shape complementarity in that region  with distinct secondary structures  extracted from the Protein Data Bank PDB; . We compWe applied our surface-centric approach to design de novo binders to target the SARS-CoV-2 RBD. First, we used MaSIF-site to predict surface sites on the RBD with a high propensity to be engaged by protein binders. We selected a site distinct from the ACE2-binding region, but overlapping such that a putative binder could inhibit the ACE2–RBD interaction Fig. . At the KD) of 4.6 µM determined by SPR  , which is much smaller than the average buried surface area of antibodies , yet still results in a high-affinity interaction. The designed interface lacks canonical hotspot residues and engages the RBD through small residues and is composed of 21% backbone and 79% side-chain contacts. Given the pandemic situation with SARS-CoV-2 and the general need for rational design of protein-based therapeutics to fight viral infections, we next engineered an Fc-fused DBR3_03 (Fc–DBR3_03) construct and tested its neutralization ability on a panel of SARS-CoV-2 variants in virus-free and pseudovirus surrogate assays28  structure of the design in a complex with the trimeric spike protein at a local resolution of 2.9 Å Fig. –12. The ure Fig. . This res28 Fig. . Neutrals28 Fig. . A cryo-s28 Fig. –15. Impos28 Fig. , which is28 Fig. , but ints28 Fig. . Our res29. We used MaSIF-site to find high-propensity protein-binding sites in PD-L1 and, unsurprisingly, the identified site overlapped significantly with the native binding site engaged by PD-1 30. To further assess the optimality of each residue at the interface of the designed binder, we screened a SSM library sampling 19 positions on the basis of DBL1_03. The most relevant positions are shown in Fig. KD of 256 nM to PD-L1  and improved binding affinity by forming additional salt bridges with PD-L1  . The chemical composition of the designed interface is similar in both designs—around 59% of the surface area is hydrophobic and the remaining area is hydrophilic for DBL1_03 and correspondingly for DBL2_02. These values are comparable to those of the PD-1–PD-L1 interaction (52% hydrophobic surface), showing that we have designed interfaces with chemical compositions similar to the native interaction using a distinct backbone conformation . For each of the design targets, we selected the top 2,000 designed sequences according to several structural metrics  30. This was a promising result given that the design was not subjected to experimental optimization by in vitro evolution. We next performed an SSM experiment and observed that mutations at the predicted core interface positions  were generally deleterious for binding, supporting the structural and sequence accuracy of the design  and assembled a more comprehensive design pipeline Fig. , perform metrics  and test01) Fig.  that sho01) Fig. . To conf01) Fig. , all of 01) Fig. . The intign Fig. . Moreoveign Fig. . The preign Fig. . AlthougKD of 21.8 nM, 42.7-fold higher than the known high-affinity PD-1, which has been reported to have a true KD of 110 pM (ref. 35)  Fig.  with a p35) Fig. .We also performed experimental characterization for two other binders targeting PD-L1 DBL4_01) and CTLA-4 (DBC2_01) and observed that the binding interactions are specific to targeted sites by competition and mutagenesis experiments performed using yeast display , we can design site-specific binders using a variety of structural motifs with native-like affinities purely by computational design.36. Our incomplete understanding of the complex interplay of molecular forces that drive PPIs has greatly hindered the comprehension of fundamental biological processes as well as the ability to engineer such interactions from first principles. It has been particularly challenging for protein modelling methodologies that use discrete atomic representations to perform de novo design of PPIs14. This is in large part due to the small number of molecular interactions that are involved in most protein interfaces and to the very small energetic contributions that determine binding affinities, making physics-based energy functions less reliable37. To address this gap, we developed an enhanced data-driven framework to represent proteins as surfaces and learn the geometric and chemical patterns that ultimately determine the propensity of two molecules to interact. We proposed a new geometric deep-learning tool, MaSIF-seed, to overcome the PPI design challenge by both identifying patches with a high propensity to form buried surfaces and binding seeds with complementary surfaces to those patches. By computing fingerprints from protein molecular surfaces, we rapidly and reliably identify complementary surface fragments that can engage a specific target within 402 million candidate surfaces. This, in practice, solves an important challenge in protein design by efficiently handling search spaces of daunting scales.Physical interactions between proteins in living cells are one of the hallmarks of function38, and several other binders targeting PD-L1 and CTLA-4 were shown to be specific to the targeted sites. Using surface fingerprints, we identified structural motifs that can mediate de novo PPIs, presenting a route to expanding the landscape of motifs that can be used to functionalize proteins and that are critical for the de novo design of function.The identified binding seeds were then used as the interface driving core to design binding proteins against challenging targets: a predicted interface in the SARS-CoV-2 spike protein, which ultimately yielded a SARS-CoV-2 inhibitor, the PD-1–PD-L1 protein complex and CTLA-4, exemplifying sites that are difficult to target with small molecules due to its flat surface. Several designed binders showed close mimicry to computationally predicted models and often achieved high binding affinities after experimental optimization. In the case of purely computationally designed binders, the PD-1 binder showed low micromolar affinity without experimental optimization, which is the range of many native PPIsKD at the micromolar level). Our de novo designs agree with previous findings39 that small changes in the polar interface rim  can result in substantial differences in binding affinities. Encouragingly, by using a larger and more structurally diverse library of binding seeds together with an optimized design pipeline, we obtained several in silico-only designed binders to a variety of targets, representing a major step forward for the robust design of de novo PPIs.For all targets, the original binding seed arguably provided the principal driver of molecular recognition representing the design’s binding interface core . As MSMS produces molecular surfaces with highly irregular meshes, PyMESH (v.0.2.1)45 was used to further regularize the meshes at a resolution of 1.0 Å. For a given protein subunit that appears in a complex, we define the subunit’s buried surface as the patch that becomes inaccessible to water molecules after complex formation. As, in our implementation, a surface is defined by a discretized mesh, we compute the buried surface region as follows. The buried surface of both the subunit and the complex are first independently computed. Then, the minimum distance between every subunit surface vertex and any complex surface vertex is computed. Subunit vertices that are farther than 2.0 Å from a vertex in the surface of the complex are labelled as part of the buried surface as these vertices no longer exist in the surface of the complex. The size of buried areas was determined by computing the area of each vertex labelled as a buried surface vertex.The MSMS programNote that computing buried surface areas using this method can result in measurements that are different from those widely used in the field, which use the solvent-accessible surface area and count the buried interface of all subunits into a single value (the buried SASA area). Here we use the molecular surface  and count a single subunit. Thus, while in Extended Data Fig. 46—a fast and simple approximation to the true geodesic distance in the patch. We used a radius size of 12 Å for patches, limited to at most 200 points, which we found corresponds approximately to 400 Å2  from the centre of the patch to every vertex is computed. To compute the angular coordinate, all pairwise geodesic distances between vertices in the patch are computed, and the multidimensional scaling algorithm49, distance-dependent curvature50), and three chemical features . These features are identical to those described previously10.Each point in a patch of the computed molecular surface was assigned an array of two geometric features , and was crystallized in a complex with a partner subunit. In total, 12,002 subunits from deposited structures passed the threshold. These subunits were then clustered by sequence identity at 30% identity and up to one representative from each cluster was selected, resulting in 3,362 subunits. A matrix of all pairwise template modelling (TM) scores for this set was then computed, and a hierarchical clustering algorithm was used on this matrix to split the dataset into 3,004 subunits for the training set and 358 for the testing set.MaSIF-site was trained on a database of PPIs sourced from PRISM44 and the buried interface area was labelled as described above. The architecture of MaSIF-site  consisted of three layers of geodesic convolution. The network received as input the full surface of a protein (with batch size of 1) decomposed into overlapping patches of size 9 Å. During training, each vertex of the input was labelled with the ground truth, with a value of 1 if the vertex belonged to the buried area and a label of zero otherwise. The output of the network is a per-vertex assignment of between 0 and 1 for the prediction of that vertex on whether it belongs to the buried surface area or not. A sigmoid activation function was used as the output layer, and a binary cross function as the loss function. Adam58 was used as the optimization function. MaSIF-site was implemented in Tensorflow (v.1.12)59, and trained for 40 h on a single-GPU machine, which allowed for 43 epochs. The MaSIF-site neural network implementation in Tensorflow contains a total of 9,267 parameters.The molecular surface for each subunit was computed using MSMS10 was used to compute fingerprints for every overlapping patch in proteins of interest. MaSIF-search was trained on a dataset of 6,001 PPIs (described previously10) to receive as input the features of the target, a binder and a random patch from a different protein. MaSIF-search was designed as a Siamese neural network architecture19 trained to produce similar fingerprints for the target patch versus the binder patch, and dissimilar fingerprints for the target patch versus the random patch. To decrease the training time and improve the performance, the features of the target were multiplied by −1 (with the exception of hydropathy), turning the problem from one of complementarity to one of similarity.MaSIF-search55, PDBBind43, the ZDock benchmark56 and SabDab57. A split between the training and testing set was performed by extracting the atoms at the interface for all 6,001 PPIs and computing a TM-score between all pairs using TM-align. A hierarchical clustering algorithm was used to cluster the pairwise matrix, which was used to split the data into a training set of 4,944 PPIs and a testing set of 957 PPIs. As in MaSIF-site, each side of the interaction could consist of one or multiple chains , and we refer to each side as a subunit. In each PPI, pairs of surface vertices within 1.0 Å of each other were selected as interacting pairs.MaSIF-search was trained on a database of 6,001 PPIs in co-crystal structures sourced from PRISM10).MaSIF-search produces fingerprints for patches with a radius of up to 12.0 Å in geodesic distance from a central vertex, and is trained to make these patches similar for interacting patches and dissimilar for non-interacting patches , the inverted input features of its interacting patch (the target) and a patch randomly chosen from a different interface in the training set (the random patch)  are then selected for a second-stage alignment and rescoring. In this step, the patch is extracted from the source protein, along with all the fingerprints for all vertices in the patch . The random sample consensus (RANSAC) algorithm implemented in Open3DN × 4, where N is the number of vertices in the candidate patch (up to 200 vertices). The IPA neural network consists of five layers of one-dimensional convolution, followed by a global averaging pool layer and seven fully connected layers. The five layers of one-dimensional convolution contain 16, 32, 64, 128 and 256 filters, respectively, with a kernel size of 1 and a stride of 1, and each layer was followed by a batch normalization layer and a rectified linear unit layer. The fully connected layers contained 128, 64, 32, 16, 8, 4 and 2 dimensions. Each fully connected layer was also followed by a rectified linear unit layer, with the exception of the last layer, which was followed by a softmax layer. The network was optimized using Adam58, with a learning rate of 10−4 and a categorical cross entropy loss function.Once a candidate patch is aligned, the IPA neural network is used to score the alignment with a score between 0 and 1 on the prediction of whether the alignment corresponds to a real interaction or not. After patch alignment, each vertex in the candidate patch is matched to the closest vertex in the target patch, and three features are computed per pair of vertices: (1) 1/(distance), the Euclidean distance in 3D between the vertices; (2) the product of the normal between the vertices; and (3) 1/(fingerprint distance), the Euclidean distance between the MaSIF-search fingerprints between the two vertices. A fourth feature, which we call ‘penetration’ is computed by computing the distance between each of the vertices in the candidate patch and all the atoms in the target. Thus, the IPA neural network receives as input a vector of size The IPA neural network was trained as follows. The same dataset used for MaSIF-search, containing 4,944 PPIs and a testing set of 957 PPIs was used. For each protein pair, one protein was chosen as the target, and the patch at the centre of the interface was selected as the target patch. The partner protein along with ten randomly chosen other proteins were then aligned to it. Any alignment of the true partner within 3 Å RMSD of the co-crystal structure was considered to be a positive. Any alignment from the true partner at greater than that RMSD or of any other protein was considered to be a negative. Features were computed for all alignments and used for the IPA neural network training. The IPA neural network was trained with batches of 32 for 50 epochs.62 was used to label each residue according to their secondary structure. Fragments with ten or more consecutive residues with a helical (H) label assigned by DSSP were extracted. Each extracted helical fragment was treated as a monomeric protein, and surface features were computed for each one. MaSIF-search fingerprints and MaSIF-site labels were then computed for all extracted helices. MaSIF-seed uses both fingerprint similarity and interface propensity to identify suitable seeds. Ultimately, our binding seed database was composed of approximately 250,000 helical motifs from which 140 million fingerprints were extracted.A snapshot of the non-redundant set of the PDB was downloaded and decomposed into α-helices, removing all non-helical elements. The DSSP program63 to enable fast structural matches. Two template motifs, one consisting of two β-strands and one consisting of three β-strands, were deprived of loops and served as input to MASTER to find sets of structurally similar motifs that would ultimately become the motif dataset for MaSIF. The search allowed for a variable backbone length of 1–10 amino acids connecting the β-strands of the template. RMSD cut-offs were set at 2.1 Å and 3 Å for two-stranded and three-stranded β-sheets, respectively. Similar to the preparation of helical motifs, each β-fragment was treated as a monomeric protein and surface features were generated, followed by the generation of MaSIF-search fingerprints and MaSIF-site labels. Ultimately, our β-strand binding seed database comprised approximately 390,000 motifs from which 260 million fingerprints were extracted.To collect β-strand motifs, a snapshot of the non-redundant set of the PDB was preprocessed with the MASTER software10, we developed a pipeline to identify potential binding seeds to targets. For each target, first MaSIF-seed was used to label each point in the surface for the propensity to form a buried surface region. A fingerprint was then computed for the target site. Finally, after scanning the entire protein, the best patch was selected. In one case, the SARS-CoV-2 RBD, the fourth best site was selected as it was the site with the highest potential to disrupt binding to the natural receptor. A MaSIF-search fingerprint was then computed for the target patch, inverting the target features before inputting them to the MaSIF-search network. The Euclidean distances between the target fingerprint and the millions of fingerprints in the binding seed database were then computed, and all of the patches with a fingerprint distance below the defined thresholds were accepted. In this paper, the thresholds used were <2.0 for PD-L1, PD-1 and CTLA-4, and <1.7 for the RBD.On the basis of the different modules within the MaSIF framework10 and outputs an IPA score (described above). Those candidate binders with an IPA score of more than 0.90–0.97 in the neural network score were accepted.Once fingerprints are matched, a second-stage alignment and scoring method uses the RANSAC algorithm as described above. After RANSAC produces an alignment, the IPA neural network classifies true binders versus non-binders64, ZDock65 and ZRank2 (ref. 24). For each helix–receptor pair, the helix from the co-crystal structure was placed along 1,000 randomly selected helices from the motif database. The methods were then benchmarked to evaluate their ability to rank the correct helix from the co-crystal structure, with an alignment RMSD < 3.0 Å from the conformation of the co-crystal structure, versus the remaining 1,000 helices. Note that each helix can potentially bind in many possible orientations and, in the case of methods that were not preceded by a MaSIF-site prediction of the target site, the helix can bind at many sites on the receptor. The measured time for all methods included only the scoring time, except for MaSIF-seed, for which the alignment time was also included in the calculation.A set of transient interactions from PDBBind was scanned to identify proteins that bind to helical motifs. A binding motif was determined to be a helix if 80% of residues are helical and the total number of residues does not exceed 60. The selected complexes were filtered to remove pairs of PPIs with high homology and a set of 31 unique PPIs was used; MaSIF-search fingerprints and MaSIF-site fingerprints were subsequently computed. MaSIF-seed was benchmarked against a hybrid pipeline of existing, fast, well-established docking tools on the dataset of helix–receptor proteins: PatchDockAll of MaSIF-seed’s neural networks  were retrained for this benchmark to remove helix–receptor pairs from the training set. In each case, MaSIF-site was used to identify the patch in the target protein with the highest interface propensity, and the fingerprint for the selected patch was compared to the fingerprints of all patches in the database. The rigid orientation of each helix in the benchmark was randomly rotated and translated before any alignment. Patches were discarded if their MaSIF-search fingerprint’s Euclidean distance to that of the target site was greater than 1.7. After alignment, patches were further filtered if the IPA score was less than 0.96.64 was used to dock all 1,001 helices, setting the target site based on a specific residue using the ReceptorActiveSite flag in PatchDock. The PatchDock score was used to produce the ranking of all conformations for all 1,001 helices.On each receptor protein, MaSIF-site was used to identify and label the target site, while PatchDockZDock was run on standard parameters and its standard scoring was used similar to PatchDock.All residues outside of the MaSIF-site-selected patch were blocked using ‘compute_blocked_res_list.sh’ provided in ZDock.In this variant, the top 2,000 results from ZDock with each of the 1,001 peptides for each of the 31 receptors were rescored using ZRank2. The ZRank2 score was then used to score all of the docking poses.62, followed by computing the contribution of helical segments  to the interface. Only interfaces with less than 50% helical segments were selected. Additional filtering was performed by requiring a mean shape complementary at the interface of >0.55 and a maximum inscribed patch area of >150 Å2. From these native complexes, seeds were extracted by selecting residues within a distance of 4 Å to the receptor and extending the backbone of these residues on their N and C terminus until the DSSP annotation changed to capture complete secondary structure elements. In total, 83 complexes were collected for the benchmark.The same set of transient interactions from PDBBind was filtered for proteins interacting through non-helical motifs. The secondary structure types of the proteins were annotated with DSSPThe decoy set was constructed from 1,000 randomly selected β-strand seeds from the MaSIF-seed pipeline, containing 500 two-stranded and 500 three-stranded β motifs. The benchmark was performed similarly to the helix–receptor benchmark described above with adapting the fingerprint’s Euclidean distance cut-off to a value of 2.5 and allowing MaSIF-seed to evaluate the top two sites in each receptor. These modifications were performed for this benchmark as it increased the accuracy while still performing at least 20 times faster than comparable competing tools. Only ZDock and ZDock/ZRank2 were benchmarked in the non-helical benchmark as ZDock/ZRank2 was shown to be the best in the helical benchmark.66 implemented in scikit-learn48.In each design case, all of the top matched seeds were clustered by first computing the RMSD between all pairwise helices, computed on the Cα atoms of each pair of helices, in the segment overlapping over the buried surface area. The pairwise distances were then clustered using metric multidimensional scaling31. β-Sheet-based seeds containing >33% contact residues found in loop regions were discarded. In total, 33, 200 and 109 refined seeds were selected on the basis of the computed binding energy, shape complementarity, number of hydrogen bonds and counts of buried unsatisfied polar atoms for PD-1, CTLA-4 and PD-L1, respectively.For the one-shot protocol, seed candidates proposed by MaSIF were refined using Rosetta and a FastDesign protocol with a penalty for buried unsatisfied polar atoms in the scoring function67, two computationally designed miniprotein databases33 and one AF2 proteome prediction database68. Seeds were cropped to the minimum number of side chains making contact before grafting. Moreover, loop regions from β-sheet-based seeds were completely removed. After side-chain grafting by Rosetta (v.3.13), a computational design protocol was used to design the remaining interface. Final designs were selected for experimental characterization on the basis of the computed Rosetta binding energy, the shape complementarity, the number of hydrogen bonds and counts of buried unsatisfied polar atoms.A representative seed was selected from each solution space, and then matched using Rosetta MotifGraft to a database of 1,300 monomeric scaffolds in the case of the RBD and PD-L1 designs. For the optimized protocol, selected seeds were grafted to a database of 4,347 small globular proteins (<100 amino acids), originating from the PDBDNA sequences of designs were purchased from Twist Bioscience containing homology overhangs for cloning. DNA was transformed with linearized pCTcon2  or a modified pNTA vector with V5 tag into EBY-100 yeast using the Frozen-EZ Yeast Transformation II Kit (Zymo Research). Transformed yeast were passaged once in minimal glucose medium (SDCAA) before induction of surface display in minimal galactose medium (SGCAA) overnight at 30 °C. Transformed cells were washed in cold PBS with 0.05–0.1% BSA and incubated with the binding target for 2 h at 4 °C. Cells were washed once and incubated for an additional 30 min with the appropriate antibodies  before induction of surface display in minimal galactose medium (SGCAA) overnight at 30 °C. Induced cells were labelled in the same manner as the single designs. Labelled cells were washed and sorted using the Sony SH800 cell sorter . For combinatorial libraries and oligo pool libraries, sorted cells were grown in SDCAA and prepared similarly for two additional rounds of sorting. After the third sort, cells were plated on SDCAA agar and single colonies were sequenced. SSM libraries were sorted once, collecting both binding and non-binding populations, and grown in liquid culture for plasmid preparation. For flow cytometry analysis of single clones, data were collected with the Galios (Beckman Coulter) cytometer using Kaluza software . Flow cytometry data were analysed using FlowJo .Combinatorial libraries, SSM libraries and oligo pools were transformed as linear DNA fragments at a 5:1 ratio with linearized pCTcon2 or pNTA_V5 vector as described previously into EBY-100 yeastAfter sorting, yeast cells were grown in SDCAA medium, pelleted and plasmid DNA was extracted using the Zymoprep Yeast Plasmid Miniprep II (Zymo Research) according to the manufacturer’s instructions. The coding sequence of the designed variants was amplified using vector-specific primer pairs, Illumina sequencing adapters and Nextera barcodes were attached using an additional overhang PCR, and the PCR products were desalted using the Qiaquick PCR purification kit (Qiagen) or AMPure XP selection beads (Beckman Coulter). Next-generation sequencing was performed using the Illumina MiSeq system with appropriate read length, yielding between 0.5–1 million reads per sample. For bioinformatics analysis, sequences were translated in the correct reading frame, and enrichment values were computed for each sequence.E. coli expression was performed using BL21 (DE3) cells and IPTG induction (1 mM at OD 0.6–0.8) and growth overnight at 16–18 °C. Pellets were lysed in lysis buffer  with sonication, and the lysate was clarified and purified. All proteins were purified using the ÄKTA pure system  with either Ni-NTA affinity or protein A affinity columns followed by size-exclusion chromatography. If TEV cleavage was necessary, fused proteins were dialysed overnight at 4 °C  with excess TEV enzymes.DNA sequences were ordered from Twist Bioscience and Gibson cloning or T7 ligation was used to clone into bacterial pET21b) or mammalian (pHLSec) expression vectors. Lists of the protein binder and target constructs are provided in Supplementary Tables 1b or mam−1 for a contact time of 120 s followed by 800 s dissociation time. After each injection, the surface was regenerated using 3 M magnesium chloride (for PD-L1) or 10 mM glycine, pH 3.0 (for RBD). Data were fit with a 1:1 Langmuir binding model within the Biacore 8K analysis software .SPR measurements were performed on the Biacore 8K  system with HBS-EP+ as the running buffer  Surfactant P20, GE Healthcare). Ligands were immobilized on the CM5 chip  through amine coupling. In total, 500–1,000 response units (RU) were immobilized and designed proteins were injected as an analyte in serial dilutions. The flow rate was 30 µl min−1 and immobilized on the tips (1–2 nm immobilized). The loaded tips were then dipped into serial dilutions of either spike protein or RBD. Curves were fit using a 1:1 model on the Gator software after subtracting the background.Biolayer Interferometry measurements were performed on the Gator BLI system using the GatorOne software . The running buffer was 150 mM NaCl, 10 mM HEPES pH 7.5. Fc-tagged designs were diluted to 5 μg ml−1 in PBS (pH 7.4), and 100 µl of the sample was injected into the Superdex 75 300/10 GL column  at a flow rate of 0.5 ml min−1, and ultraviolet light (280 nm) and light scattering signals were recorded. Molecular mass was determined using the ASTRA software .Size-exclusion chromatography  with an online multi-angle light scattering device  was used to determine the oligomeric state and molecular mass of the protein in solution. Purified proteins were concentrated to 1 mg ml−1 and a response time of 0.125 s. All spectra were averaged twice and corrected for buffer absorption. Temperature ramping melts were performed from 20 to 90 °C with an increment of 2 °C min−1. Thermal denaturation curves were plotted by the change of ellipticity at the global curve minimum to calculate the melting temperature (Tm).Far-ultraviolet circular dichroism spectra were measured using a Chirascan spectrometer (AppliedPhotophysics) in a 1 mm path-length cuvette. The protein samples were prepared in a 10 mM sodium phosphate buffer at a protein concentration of between 20 and 50 µM. Wavelengths between 200 nm and 250 nm were recorded with a scanning speed of 20 nm min5 cells were incubated with 50 µl Fc Block  that was prediluted 1:50 in FACS buffer  and 2% BSA ) for 15 min on ice. The samples were subsequently supplemented with 50 µl of PD-L1 binders prepared as follows. High-affinity PD-1_Fc: serially diluted 1:2 for 20 dilutions in FACS buffer, starting at 62.5 µg ml−1; DBL1_03_Fc and DBL1_04_Fc: serially diluted 1:2 for 16 dilutions in FACS buffer, starting at 125 µg ml−1; DBL1_03_KO_Fc and PD-1_Fc: serially diluted 1:2 for 14 dilutions in FACS buffer, starting at 125 µg ml−1. The cell solutions were incubated for 30 min. The samples were then washed three times, resuspended in 100 µL of FACS buffer containing secondary R-PE goat anti-human IgG antibody diluted 1:100  and incubated for 30 min. The samples were then washed three times to remove unbound antibody, resuspended in 100 µl of FACS buffer, and analysed using the LSR Fortessa flow cytometer (BD Biosciences).Karpas-299 cells were purchased from Sigma-Aldrich (06072604-1VL) with the approval of the European Collection of Authenticated Cell Cultures (ECACC). Cells were authenticated (PCR) and tested negative for mycoplasma contamination (PCR & Vero indicator) by the provider. For flow cytometry analysis of DBL1 designs binding to PD-L1 on Karpas-299 cells, 2 × 10Q9NZQ7; from Phe19 to Arg238) was overexpressed as inclusion bodies in the BL21 (DE3) strain of E. coli. Renaturation and purification of PD-L1 was performed as previously described70. In brief, inclusion bodies of PD-L1 were diluted against a refolding buffer , 0.5 mM glutathione disulfide (GSSG)) at 4 °C for 24 h. The PD-L1 was then concentrated and exchanged into a buffer of 20 mM Tris-HCl (pH 8.0) and 15 mM NaCl and further analysed using HiLoad 16/60 Superdex 75 pg (Cytiva) chromatography. PD-L1 binder designs (DBL1_03 and DBL2_02) were overexpressed in E. coli as inclusion bodies. Renaturation and purification of the PD-L1 binder designs were performed as for the PD-L1 protein. PD-L1 and binder designs were then mixed together at a molar ratio of 1:2 and incubated for 1 h on ice. The binder–PD-L1 complex was further purified by HiLoad 16/60 Superdex 75 pg (Cytiva) chromatography.PD-L1 extracellular domain fragment (UniProt: −1) was mixed with 1 μl of crystal growing reservoir solution. The resulting mixture was sealed and equilibrated against 100 μl of reservoir solution at 4 or 18 °C. Crystals of the DBL1_03–PD-L1 complex were grown in 0.2 M potassium formate and 20% (w/v) PEG 3350. Crystals of the DBL2_02–PD-L1 complex were grown in 0.2 M potassium/sodium tartrate, 0.1 M Bis-Tris propane, pH 6.5 and 20% (w/v) PEG 3350. Crystals were flash-cooled in liquid nitrogen after incubating in anti-freezing buffer (reservoir solution containing 20% (v/v) glycerol). Diffraction data of crystals were collected at Shanghai Synchrotron Radiation Facility (SSRF) BL19U. The collected intensities were subsequently processed and scaled using the XDS package71 . The structures were determined using molecular replacement with the program Phaser MR in PHENIX (v.1.20.1-4487), with the reported PD-L1 structure (PDB: 3RRQ) as the search model72. COOT (v.0.9.5) and PHENIX (v.1.20.1-4487) were used for subsequent model building and refinement74. The stereochemical qualities of the final model were assessed using MolProbity75 (v.4.5.1). Details of data collection and refinement statistics are shown in Extended Data Table For crystal screening, 1 μl of binder–PD-L1 complex protein solution  nonlinear four-parameter curve fitting analysis of the log[agonist] versus response.Luminex beads were prepared as previously published28. In brief, VeroE6 cells were seeded into 96-well plates the day before the infection. The DBR3_03-Fc compound in serial dilutions was mixed with Omicron-spike virus and incubated at 37 °C for 1 h before addition to the cells. The cells with virus were kept a further 48 h at 37 °C, and then washed and fixed for crystal violet staining and analysis. Neutralization EC50 calculations were performed using Prism  nonlinear four-parameter curve fitting analysis.The virus neutralization assays were performed as previously published−1 of the spike(D614G)–binder sample or the spike(Omicron)–binder sample were applied onto glow-discharged carbon-coated copper grids , blotted for 4.0–8.0 s and flash-frozen in a liquid ethane/propane mixture cooled to liquid nitrogen temperature, using the Vitrobot Mark IV (Thermo Fisher Scientific) with 100% humidity and the sample chamber operated at 4 °C. The grids were screened in the Thermo Fisher Scientific 200 kV Glacios cryo-EM instrument. Suitable grids were transferred to TFS Titan Krios instruments for data collection. Cryo-EM data collection statistics of this study are summarized in Extended Data Table − Å−2 over approximately 3 s, corresponding to a dose rate of 4.53 e− px−1 s−1. For spike(Omicron)–binder data, 22,266 videos were recorded on the Titan Krios G4 microscope, equipped with TFS SelectrisX imaging filter and Falcon4 camera. Exposures were collected at 60 e− Å−2 total dose with a physical pixel size of 0.726 Å per pixel over approximately 6 s, corresponding to a dose rate of 5.4 e− px−1 s−1, at a defocus range of −0.8 to −2.5 μm. Data were analysed using cryoSPARC (v.3.3.1)76.For cryo-electron microscopy investigations, 3.0 µl aliquots at a concentration of 0.87 or 1.0 mg ml76 and gain-normalized, motion-corrected and dose-weighted using the cryoSPARC implementation of patch-based motion correction. CTF estimation was performed using the patch-based option in cryoSPARC. A small set of particles was manually selected and followed by 2D classification to create a 2D template for the subsequent automatic particle picking. For the sample of spike(D614G) in complex with the de novo designed binder, 832,816 particles were automatically selected by a template-based picker and processed for three rounds of 2D classification, resulting in a particle set of 184,763 particles. The particles were grouped into three classes, using the ab initio and hetero-refine implementations in cryoSPARC. The best 3D class, comprising 97,804 particles, was further processed for another round of ab initio reconstruction and hetero-refinement. The well-resolved class consisting of 67,432 particles resulted in a 2.6 Å overall resolution global map in C1 symmetry. The binder–RBD region was refined with a soft mask, resulting in a local map at 3.1 Å resolution. For the data processing of the spike(Omicron)–binder complex sample, 1,820,333 particles were picked using the cryoSPARC template-based picker. After two rounds of 2D classifications, 981,561 particles were selected and processed for ab initio reconstruction and hetero-refinement, resulting in a set of 595,599 particles. Subsequently, the selected particle set was classified by multiple rounds of 3D classifications in cryoSPARC. The best-resolved 3D class, containing 50,758 particles, resulted in a 2.8 Å overall resolution map and the binder–RBD region was further improved by performing focused refinement with a soft mask, resulting in a map at a resolution of 3.3 Å. The resolution for all 3D maps was estimated based on the Fourier shell correlation with a cut-off value of 0.143.Details of the image processing are shown in Supplementary Figs. 7BNO; spike(D614G)) was used for the region of spike(D614G) as a starting model. The model was rigid-body fit into the cryo-EM density in UCSF Chimera77 and adjusted manually in Coot (v.0.9.4)78. De novo building for the binder parts was performed manually in Coot (v.0.9.4). For building the spike(Omicron)–binder structure, the model ) was fitted into the density and rebuilt and adjusted manually using UCSF Chimera and Coot (v.0.9.4). After the structural rebuilding, all of the atomic models were refined using the Phenix (v.1.19.2-4158) implementation of real.space.refine with general structural restraints80. Comprehensive validation (cryo-EM), model quality assessment and statistics are provided in Extended Data Table 81 and Pymol .For model building of the spike(D614G)–binder, the previous model (PDB: x v.1.19.-4158 impFurther 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/s41586-023-05993-x.Supplementary InformationSupplementary Figs. 1–23 and Supplementary Tables 1–7.Reporting SummaryPeer Review File


Mesenchymal–epithelial transition (MET) factor is a proto-oncogene encoding tyrosine kinase receptor with hepatocyte growth factor (HGF) or scatter factor (SF). It is found on the human chromosome number 7 and regulates the diverse cellular mechanisms of the human body. The impact of mutations occurring in the MET gene is demonstrated by their detrimental effects on normal cellular functions. These mutations can change the structure and function of MET leading to different diseases such as lung cancer, neck cancer, colorectal cancer, and many other complex syndromes. Hence, the current study focused on finding deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) and their subsequent impact on the protein's structure and functions, which may contribute to the emergence of cancers. These nsSNPs were first identified utilizing computational tools like SIFT, PROVEAN, PANTHER-PSEP, PolyPhen-2, I-Mutant 2.0, and MUpro. A total of 45359 SNPs of MET gene were accumulated from the database of dbSNP, and among them, 1306 SNPs were identified as non-synonymous or missense variants. Out of all 1306 nsSNPs, 18 were found to be the most deleterious. Moreover, these nsSNPs exhibited substantial effects on structure, binding affinity with ligand, phylogenetic conservation, secondary structure, and post-translational modification sites of MET, which were evaluated using MutPred2, RaptorX, ConSurf, PSIPRED, and MusiteDeep, respectively. Also, these deleterious nsSNPs were accompanied by changes in properties of MET like residue charge, size, and hydrophobicity. These findings along with the docking results are indicating the potency of the identified SNPs to alter the structure and function of the protein, which may lead to the development of cancers. Nonetheless, Genome-wide association study (GWAS) studies and experimental research are required to confirm the analysis of these nsSNPs. Mesenchymal–epithelial transition (MET) factor gene, which is also called c-MET, is a proto-oncogene that encodes a receptor tyrosine kinase for hepatocyte growth factor (HGF) or scatter factor (SF). It belongs to the MNNG HOS (N-methyl-N′-nitroso-guanidine human osteosarcoma) transforming gene family and is situated on human chromosome number 7 7q21–q31) [–q31 [1, The extracellular and intracellular portions of MET protein contain several domains. HGF binding to MET protein has an effect on receptor dimerization and phosphorylation of tyrosine residues of the protein, which eventually leads to the phosphorylation of intracellular docking sites . MET genSingle-nucleotide polymorphisms (SNPs) are the most prevalent type of genetic alteration or mutation, affecting a single base pair in alleles in one or more individuals . A subgrA concentrated comprehension of how MET gene activation manages tumor development will need additional investigation utilizing 3D molecular configurations, cell culture systems, human tumors, and also animal prototypes. With the help of these combined techniques, and also with novel medications targeting MET gene, shortly, the impact of MET on tumorigenesis will be recognized and may be controlled in a better way . CorrelaThis study aims to determine and evaluate the effects of deleterious nsSNPs on the structure, function, and other various properties of MET gene, which can eventually lead to oncogenesis. The major intents of this research work are to identify and analyze the most deleterious nsSNPs of MET gene. Earlier the effects of oncogenic mutation on isocitrate dehydrogenase 1 , the impIn the present study, the nsSNPs or missense variants were acquired from the SNP dataset of MET gene for analyzing their functional and structural effects of them. The most deleterious nsSNPs were identified and analyzed their impacts on normal properties of MET. Different types of computational tools, which are now broadly involved with identifying missense mutations, were utilized for the ample identification and analysis of damaging nsSNPs in this research work. In previous different studies, these tools were utilized, which helped us in selecting a research workflow synchronized with these tools. They were widely used for the first inspection of prospective candidates in many other research experiments related to mutation analysis , 21. EacThe damaging effect of non-synonymous mutations in the structure and function of the MET protein was predicted and evaluated using various bioinformatics tools. An outline of the computational approaches used in this study is illustrated in https://www.ncbi.nlm.nih.gov/). The comprehensive dataset of SNP mutations of the MET gene was retrieved from the dbSNP database of NCBI (https://www.ncbi.nlm.nih.gov/snp/). The protein sequence (P08581) of MET gene was retrieved from UniProtKB database (https://www.uniprot.org/).All data about the MET gene were collected from a web-based source, National Centre for Biotechnology Information (NCBI)  Sorting Intolerant from Tolerant. Sorting Intolerant from Tolerant (SIFT) is a web-based tool (https://sift.bii.a-star.edu.sg/) [edu.sg/)  that anaedu.sg/) . SIFT seedu.sg/) . This predu.sg/) . The est(2) Protein Variation Effect Analyzer. Protein Variation Effect Analyzer (PROVEAN) is an online tool (http://provean.jcvi.org/) [vi.org/)  for the vi.org/) . It can vi.org/) . The PROvi.org/) . The inpThe functional consequences of these nsSNPs of MET gene accumulated from dbSNP–NCBI database were analyzed by two in silico tools, i.e., PolyPhen-2 and PANTHER-PSEP server.These tools will determine the damaging effects of non-synonymous mutations.(1) Polymorphism Phenotyping v2. Polymorphism Phenotyping v2 (PolyPhen-2) server is an acclaimed bioinformatics tool (http://genetics.bwh.harvard.edu./pph2/) [./pph2/) , which i./pph2/) . The FAS(2) PANTHER-PSEP. PATHER-position-specific evolutionary preservation, version 17.0 (PANTHER-PSEP) is a web-based tool (http://www.pantherdb.org/tools/csnpScoreForm.jsp) [orm.jsp) , which iorm.jsp) . The preorm.jsp) . FASTA sThe missense or nsSNPs can change the stability of the protein, which directly has an effect on protein function. So, it is essential to evaluate the effects of changing the stability of deleterious mutations . Hence, https://folding.biofold.org/i-mutant/i-mutant2.0.html) is a standard programmed tool that is developed on the basis of support vector machine (SVM) and used for the evaluation of effects of protein stability changes for missense or non-synonymous mutations [ΔΔG values upon missense mutation [I-Mutant 2.0 web server  is an online tool that is used for predicting stability changes of protein upon mutations [ΔΔG values [MUpro  that advances the positioning of pathogenic nsSNPs over prevailing approaches, can generate mechanisms involved with potentially causative of ailment, and proceeds pathogenicity score on specific genomes [Primarily, for characterizing mutations to analyze complex diseases, bioinformatics methods have been developed, which can identify the impact of pathogenicity of mutations by predicting statistical effects of nsSNPs on structure and function of protein. MutPred2 (MutPred 2.0) is a computational tool ( genomes . The FAShttps://consurf.tau.ac.il/) [The phylogenetic or evolutionary conservational score of an amino acid extremely influences the structure and function of a protein. This type of attribute of proteins is essential for interpreting the mutations that have a deleterious effect . Hence, .ac.il/)  is an on.ac.il/) . It esti.ac.il/) . To analhttp://bioinf.cs.ucl.ac.uk/psipred/) [Secondary structures of proteins are considered native structural attributes that are alleviated through hydrogen bonds on the backbone and are also regarded as the linking networks between primary sequences and tertiary structures of proteins . The twosipred/)  is usualsipred/) . The FAShttps://pfam.xfam.org/) [Functional domains of a protein can be predicted using a database named Pfam (am.org/) . Pfam isam.org/) . It compam.org/) . The acchttps://www.musite.net/) [Post-translational modifications (PTMs) imply amino acid change with the addition of various side chains in proteins. These changes or modifications occur because of essential governing procedures to manage various cellular activities. Hence, PTMs have a major influence on the structure and function of proteins. Interference in these PTM sites can direct the disturbance of important biological mechanisms, which can cause several diseases . In thiste.net/)  was usedte.net/) . The FAShttp://raptorx.uchicago.edu/BindingSite/) [The interfaces between proteins and ligands ensue through amino acid residues at particular sites of the protein, which are generally positioned in pocket-like regions. These regions are known as ligand binding sites. The ligand binding sites have enormous importance in the fields of molecular dynamics, molecular docking, and drug designing. That's why the prediction of binding sites is essential for discovering the intermolecular procedures as well as for understanding the pathogenicity of diseases . In thisngSite/)  was usedngSite/) . On the ngSite/) . The seqhttps://www3.cmbi.umcn.nl/hope/, version 1.1.1) server was used [To predict the effects of deleterious nsSNPs on the properties of protein such as residue size, charge, hydrophobicity, structure, etc., HOPE (was used . HOPE sewas used . The prohttps://www.rcsb.org/) [PyMOL software (PyMOL 2.5) was used for the homology modeling of variants of MET protein. For modeling of the variants, the structure of the MET protein  (PDB ID sb.org/) .https://bioinfo3d.cs.tau.ac.il/PatchDock/) [https://bioinfo3d.cs.tau.ac.il/FireDock/) server was used for refinement. It marks the flexibility problem and solutions scoring generated by rigid body docking systems [Molecular docking is used to demonstrate the interaction between receptor and ligand molecules to analyze the effects of mutant complexes on binding affinity with their ligands. In this study, PatchDock (chDock/)  server wchDock/) . For molchDock/)  were ent systems .https://genemania.org/) that uncovers other genes associated with a set of genes that are submitted as input, expending a very large set of functional association data. These data comprise protein and genetic interactions, pathways, co-expression, co-localization, and also protein domain similarity [Gene Multiple Association Network Integration Algorithm (GeneMANIA) is a web tool  that combines gene–gene and/or protein–protein interactions into a network frame, with interfaces has their score of confidence [Search Tool for the Retrieval of Interacting Genes (STRING) is an online database  [The Kaplan–Meier Plot   is an onalysis/) . This plalysis/) . The preA total of 45359 SNPs were obtained from the dbSNP database of NCBI. The obtained SNPs contain different variations. Among the 45359 nsSNPs, 1306 missense or non-synonymous, 571 synonymous, 2672 non-coding transcripts, 2956 coding transcripts, 520 3-prime UTR regions (Untranslated region), 478 5-prime UTR regions, 43163 intron, 5768 genic downstream transcript, 8653 genic upstream transcript, and other variants were found ((1) Sorting Intolerant from Tolerant. The SIFT server postulates that the amino acids that are significant for protein function will be conserved and so then modifies at specific positions that are likely to be determined as deleterious mutations [utations . It estiutations . The pre(2) Protein Variation Effect Analyzer. PROVEAN server utilizes the principal sequence of the protein and its sequence homologies, which are investigated by dint of BLAST in NCBI database. The prediction of PROVEAN is based on a scoring system where the cut-off value is −2.5 [ is −2.5 . On the  is −2.5 . Deleter(1) Polymorphism Phenotyping v2. The PolyPhen-2 server accomplishes the prediction on the basis of a number of protein sequences, phylogenetic, and structural characteristics distinguishing amino acid substitutions. It extricates several sequences and structural attributes of amino acid substitutions and analyzes the probability of their damaging effect [g effect . PolyPheg effect . The preg effect . After t(2) PANTHER-PSEP. PANTHER-PSEP utilizes a metric-related arrangement, which is distinct from evolutionary preservation, whereas the potential sequences of proteins from the ancestral source at joints of a phylogenetic tree are restructured on the basis of homologous proteins [proteins . It can proteins . The PSEThese two datasets of outcomes of PolyPhe-2 and PATHER-PSEP servers are distinguishable on the basis of their possible outcomes. PolyPhen-2 produces two different datasets based on a scoring system with sequences and structural attributes, whereas PATHER-PSEP constructs a range of outcomes on the basis of homologous proteins of a phylogenetic tree.ΔΔG) of the protein [ΔΔG value, whereas ΔG (mutant protein)—ΔG (wild protein) in kcal/mol, which is evaluated at pH −7 and 25°C temperature [ΔΔG value is negative or less than “0” and 17 nsSNPs were with increased stability, where ΔΔG value is positive or greater than “0” , and 5 nsSNPs were found with increased stability  , only 18 nsSNPs were identified as the most deleterious mutations .MutPred2 server predicts molecular pathways associated with a pathogenic amino acid substitution . It grouThe phylogenetic conservation analysis of amino acids imparts a better interpretation of the importance of a specific amino acid residue and its evolutionary structure . The conSecondary structures of MET protein were predicted using the PSIPRED server based on PSI-BLAST. The predicted sequence outline revealed the arrangement of alpha helix, beta sheet, extracellular, and coil structures. After the analysis of 18 amino acid substitutions by PSIPRED server, three different types of secondary structures were found. Among the 18 residues, 6 (33.33%) were predicted as “coil”, 6 (33.33%) were predicted as “helix”, and 6 (33.33%) were predicted as “extracellular” structures .Pfam database revealed the domains of MET protein upon the submission of the protein ID of MET. Six domains of MET protein were found and these domains were arranged between 317–493, 519–562, 563–654, 657–738, 742–833, and 1078–1337 amino acid residues, correspondingly . Among tThe MusiteDeep server predicts PTM sites by using only protein sequences as input material and it results from a real time estimation for several proteins. The outcome is displayed at the level of amino acid residue for the various selected PTM sites . This seThe RaptorX server is different from other web servers by the value of the alignment amid a target protein sequence and distantly linked template proteins and also by a non-linear scoring system . In thisThe HOPE server analyzed the effects of highly damaging nsSNPs on the properties of MET protein. In this study, 18 deleterious nsSNPs of MET were analyzed by this server based on different properties such as size, charge, hydrophobicity, etc. Among these 18 substitutions, nine mutants were found bigger than wild residues, while seven mutants were found smaller than wild residues. Furthermore, seven substitutions were less hydrophobic than wild types, whereas five substitutions were more hydrophobic than wild types. Besides, two mutations  turned neutral to positively charged, while one mutation (G757E) turned neutral to negatively charged. On the other hand, three substitutions  turned negatively charged to neutral  were selected because these variants were among 18 highly damaging residues. None of these lies in the binding pockets of MET gene as predicted by RaptorX-Binding site server. However, earlier experiments confirmed that 25–519 residues of MET protein are sufficient for binding HGF/SF, while 567–932 residues increase the binding affinity . All theTo analyze the effect of mutations on the structure and functions of MET protein, molecular docking analysis was carried out with these two specific NK1 and NK2, which are variants of HGF. Fourteen complexes were utilized and these are 2 native complexes (MET-NK1 and MET-NK1) and 12 mutant complexes  for this analysis. PatchDock server was used for calculating binding energy and FireDock server for further refinement. Comparing the binding energy of native between mutant complexes, the mutant A364T-NK1 complex showed the weakest binding affinity with a binding energy of 6.31 when assessed with native and other mutant complexes . This anGeneMANIA is a large collection of interaction networks from several data sources which identify genes and networks that are functionally associated . This seProtein–protein interactions were investigated to interpret all functional interactions among cellular proteins . The STRThe clinical analysis discovered several consequences of MET gene deregulation in various kinds of cancers, i.e., lung, breast, ovarian, and gastric cancer. According to the results of the clinical investigation through Kaplan–Meier, MET gene deregulation was correlated with the survival rate of lung and gastric cancer patients. The expression curves in the lungs and gastric cancer plot had significant distances which defined the association of MET deregulation with the survival rate of cancer patients. But the expression level of both breast and ovarian cancer does not affect the survival rate of patients, since both of the expression curves were overlapped . TherefoThe tertiary structure of a protein determines its function, hence any alteration to its amino acid sequence has the potential to alter the protein's structure and cause disease. The MET receptor with its ligand HGF synchronizes a range of functions of cells, where many cellular functions can be disturbed in human cancers. Disturbance in the MET signaling pathway can direct cellular movement and scattering, angiogenesis, proliferation, invasion, and eventually to metastasis . Using vΔΔG. Even a substitution with a ΔΔG value except zero can cause substantial changes in the protein according to the comparative values of ΔG and ΔΔG [ΔΔG value may not cause prominent structural changes in a protein with a large ΔG value. Moreover, a few damaging mutations can be alleviated, which specifies that estimating pathogenicity via a particular method is very unreliable [This analysis commenced with the separation of 1306 missense or nsSNPs from the 45359 SNPs of MET gene collected from the dbSNP database of NCBI. Then the MET protein sequence, accumulated from the UniProtKB database for conducting further analysis to identify the deleterious nsSNPs in MET gene. The functional and structural changes in protein occurred due to highly damaging nsSNPs, which were identified from the results acquired from various servers. At first, functional analysis of nsSNPs was carried out by some prominent computational tools, i.e., SIFT, PROVEAN, PolyPhen-2, and PANTHER-PSEP. In SIFT server, 55 deleterious and 109 neutral mutations were found among the total 164 amino acid substitutions, which have given the SIFT result. While in the PROVEAN, 39 and 125 mutations showed a deleterious and neutral effect on protein, respectively. On the other hand, PolyPhen-2 server showed two datasets of results, these are 98 and 66 damaging and benign mutations, respectively, in HumDiv dataset, whereas 80 and 84 damaging and benign mutations, respectively, in HumVar dataset. The PANTHER-PSEP server displayed 126 damaging and 38 benign mutations among 164 substitutions. Afterward, structural stability changes of MET protein were analyzed because the protein stability has a significant effect on the function and activity of proteins. Here, I-Mutant 2.0 and MUpro server were used for the prediction of stability changes of MET. In the I-Mutant 2.0 server, 147 mutations with decreased stability and 17 mutations with increased stability were found. While in the MUpro server, 159 and 5 mutations with decreased and increased stability, respectively, were found. But cautiousness should be maintained while analyzing the substitutions on the basis of reliable .Then the results obtained from the above six servers were analyzed and compared, which eventually assorted 18 potentially deleterious mutations for MET protein. These 18 deleterious mutations went through the remaining six servers, i.e., MutPred2, ConSurf, PSIPRED, Pfam, MusiteDeep, RaptorX, and Project HOPE for further structural scrutiny. The MutPred2 predicted the statistical significance of nsSNPs on the structure and function of MET protein, where different characteristics of deleterious nsSNPs were revealed. The ConSurf server determined the conservational score of the MET protein for different amino acid residues, where 11 residues with score 9, 2 residues with score 8, 2 residues with score 7, 2 residues with score 6, and 1 residue with score 5. In conservation analysis, score 9 denotes highly conserved amino acid, and score 1 denotes the least conserved or variable amino acid. The highly conserved residue is predicted to be a highly damaging residue. The PSIPRED server estimated three types of secondary structures among the 18 deleterious substitutions, whereas six residues with coil structure, six residues with helix, and six residues with extracellular structure. After that, Pfam database showed six functional domains of MET protein upon submission of the protein accession number. The MusiteDeep server is able to predict the PTM sites, where only one amino acid residue (Y1230) as phosphorylation site in the prominent PTM sites among the 18 deleterious nsSNPs, which signifies the alteration in PTMs of MET protein due to Y1230H substitution. Then the RaptorX-Binding server determined the possible ligand binding sites of the MET protein. Any kind of change or variation at the ligand binding site of the protein can neutralize or reduce the activity of the protein. Among the 18 mutations, only one substitution (M1131T) was found in the binding sites of MET protein.Since, the mutations can cause alterations in size, charge, and hydrophobicity of residue, which may lead to disruption of protein structure and interactions. The HOPE server can compare the wild and mutant residues based on different properties of the protein. From the result of HOPE, nine residues were bigger than the wild and seven residues smaller than the wild type; two positively charged, one negatively charged, and three neutral; seven residues were less hydrophobic; and five residues were more hydrophobic than the wild type among 18 deleterious substitutions.β) chain. Hence, it is feasible that the HGF or SF binding site is confined within this protein sequence. Within the longest structures, the strongest binding affinity was detected, but the binding affinity of 25–519 residues and 25–567 residues was easily assessable. On the other hand, 567–932 exhibited no binding affinity. As a result, the N-terminal portion of the MET ectodomain (25–519) is adequate for binding with HGF or SF, while the C-terminal part (567–932) has no binding affinity, but stimulates the binding affinity to the N-terminal portion (25–519). Therefore, after the structural analysis, six variants  among 18 deleterious variants of MET protein were selected for homology modeling through PyMOL software, because MET protein has shown strong binding affinity between 25–519 residues. The modeled mutant variants then went through molecular docking via PatchDock and FireDock servers for analyzing the binding affinity of native and mutant complexes. The mutant complex A364T-NK1 with 6.31 binding energy showed the least binding interaction than native and other mutant complexes. This assessment showed that the mutant complexes can show the least binding interactions than native complexes on the basis of their deleterious effect. On the other hand, similar in silico analysis of the HGF gene revealed that five nsSNPs  of the HGF are the most deleterious that hinder MET–HGF interaction [The first 519 amino acid residues of MET protein are essential for HGF or SF binding, which comprises the first 212 amino acid residues of the beta  survival percentage is not influenced by the deregulation of the MET gene. Considering that the deleterious nsSNPs of MET have a major influence on the structure and function of MET protein, they can affect the normal cellular functions in MET dysregulation. Among the identified 18 deleterious nsSNPs, most nsSNPs have no existing literature. Only 5  of 18 deleterious nsSNPs have been found associated with resistance mechanisms to MET kinase inhibitors, which ultimately affects cancer treatments , 68. TheIn this research work, the mutations or variations found in MET gene were explored and screened, which may have detrimental effects on human normal cellular functions, preceding different cancers, and other diseases. Since laboratory experiments on mutations require a huge time and labor, this computational study was carried out to identify the most deleterious mutations, for further research purposes. However, none of these predicted deleterious nsSNPs were found to be well studied in the broad spectrum. All the computational tools have shown us simply the predicted results. Yet, the sophistication of these algorithms solely depends on raw experimental data. The downstream structural and functional analysis may be faulty as a result of unreliable and inaccurate raw data. Hence, it is advised to employ a number of tools and come to a decision by comparing the outcomes of these tools. Furthermore, some computational tools may not be usable over time or developed with newer versions, which can make the experimental outcomes less specific. Also, several in vitro and in vivo studies should be used in the laboratory to validate the outcomes of the bioinformatics analysis.Since the alterations due to non-synonymous mutations in MET gene have a negative effect on human cellular functions and can cause diverse types of cancers in humans, the whole screening of these mutations was required. The present study focused on the analysis of the detrimental effects of deleterious nsSNPs or mutations in MET. Eighteen most deleterious nsSNPs were detected in MET among 45359 SNPs for further practical field application in research intent. The effects on binding affinity also imply that mutation prompts changes in wild type proteins. The altered structure and function of MET protein can lead to dysregulated activities of the MET gene, which was further supported by the docking results that may induce complex diseases. Along with this, changes in structural stability can alter the conformation and other properties of proteins concerning cancer. These nsSNPs may have implications for treatment approaches and personalized medicine, and they may be employed in future experimental research to examine how they contribute to the pathophysiology of associated diseases.


Shotgun proteomics is essential for protein identification and quantification in biomedical research, but protein isoform characterization is challenging due to the extensive number of peptides shared across proteins, hindering our understanding of protein isoform regulation and their roles in normal and disease biology. We systematically assess the challenge and opportunities of shotgun proteomics-based protein isoform characterization using in silico and experimental data, and then present SEPepQuant, a graph theory-based approach to maximize isoform characterization. Using published data from one induced pluripotent stem cell study and two human hepatocellular carcinoma studies, we demonstrate the ability of SEPepQuant in addressing the key limitations of existing methods, providing more comprehensive isoform-level characterization, identifying hundreds of isoform-level regulation events, and facilitating streamlined cross-study comparisons. Our analysis provides solid evidence to support a widespread role of protein isoform regulation in normal and disease processes, and SEPepQuant has broad applications to biological and translational research. Protein isoform quantification in shotgun proteomics is challenging due to the mapping of many peptides to multiple protein isoforms. Here, the authors present a computational method SEPepQuant and demonstrate its utility in revealing protein isoform level regulation in shotgun proteomics. This process is highly regulated, and dysregulated RNA splicing has been linked to a wide range of diseases such as retinal and developmental disorders, neurodegenerative diseases, and cancer4. High-throughput sequencing-based transcriptomic studies have shown that most human protein-coding genes undergo alternative splicing to produce multiple mRNA isoforms5. Mass spectrometry (MS)-based shotgun proteomics is the primary method for protein identification and quantification from biological samples6, but shotgun proteomics studies have provided very limited information on protein isoforms due to intrinsic challenges in data analysis. In fact, the extent to which transcript isoform complexity propagates to the proteome remains controversial9, and systematic investigation of the roles of protein isoforms in normal and disease biology is largely lacking10.Alternative splicing of precursor messenger RNA (pre-mRNA) is an essential post-transcriptional process that is believed to underlie increased cellular and functional complexity in eukaryotic organisms11, Trans-proteomic pipeline12, OpenMS13, FragPipe15, among others. Despite algorithmic and implementation differences, these tools share a similar workflow. First, observed MS/MS spectra are searched against a reference protein database for peptide identification. Next, identified peptides are used to infer a list of proteins that are assumed to be present in the sample, a process known as protein inference. Finally, inferred proteins are quantified based on the signal intensity measurements of the constituting peptides.In a shotgun proteomics experiment, proteins extracted from biological samples are digested into peptides using enzymes such as trypsin and then analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Each LC-MS/MS run generates thousands of spectra, which serve as the basis for the identification and quantification of peptides and proteins. Many bioinformatics tools have been developed to perform these essential computational tasks in shotgun proteomics data analysis, such as MaxQuant17. For protein quantification, peptides shared by multiple protein groups are either ignored or assigned to the group with the largest number of associated peptides, i.e., defined as razor peptides by MaxQuant11. Typically, one representative protein  is selected from each protein group for reporting and downstream analysis18. This parsimonious approach plays a critical role in preventing overstating the number of proteins in protein inference, however, it also limits the potential for protein isoform characterization. First, protein isoforms without uniquely identified peptides are essentially ignored. Secondly, the assignment of shared peptides to the protein groups and proteins with the largest number of associated peptides for quantification may not necessarily be the correct solution.The difficulty in protein isoform identification and quantification is complicated by the large number of degenerate peptides, which are peptides that can be mapped to multiple proteins due to a high level of sequence similarity between protein isoforms encoded by the same gene, or genes in the same gene family. The current most adopted practice in the field is to collapse proteins with the same set of supporting peptides together with those that are supported by a subset of these peptides into a protein group19 and FragPipe and used in some recent studies22. gpGrouper uses quantities of gene-specific peptides to guide the split of quantities of shared peptides. Because only a small proportion of peptides are shared between genes, it makes full use of both unique and shared peptides to produce gene-level quantification with demonstrated accuracy19. However, protein isoform information is ignored in this approach. Along the same line, SCAMPI uses statistical modeling to generate quantification for individual proteins using both unique and shared peptides23. This approach was demonstrated when proteomics data were searched against the UniProt canonical database in which only canonical protein sequences are included, which means a single canonical sequence for most genes. For example, although there are eight annotated isoforms of TP53 in UniProtKB, the canonical database only included a single canonical isoform. Therefore, isoform reduction occurred in database construction, and the challenge addressed was primarily to distribute quantities of peptides shared by different genes. The method’s ability to handle search results from a comprehensive protein database is uncertain because the inclusion of canonical and alternative isoform sequences leads to a substantial reduction in the number of isoform-specific peptides, which are crucial for accurately distributing the quantities of peptides shared between isoforms. Moreover, it is also unclear whether the method can be directly applied to ratio data generated from labeled experiments such as tandem mass tag (TMT)-base experiments.To address the challenge in protein isoform discrimination, several methods have been developed. One solution is to perform gene-based quantification, which is implemented in tools such as gpGrouper24, whereas Peptide Correlation Analysis (PeCorA) focuses on outlier peptides to reveal differential proteoform regulation25. These methods require multiple samples for analysis, and peptides annotated to more than one protein are excluded from the analysis. Therefore, many peptides would be excluded when a comprehensive protein database covering both canonical and alternative isoform sequences are used for database searching.Methods have also been developed based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Protein Quantification and Peptide Quality Control (PQPQ) selects peptides correlating over samples to improve the quantitative accuracy and precision26. This method also enables novel protein isoform identification, but the quantification challenge remains because each gene may still have multiple RNASeq data-supported isoforms. Based on a strong assumption that each gene only has a dominant isoform, Liu et al.10 used matched RNASeq data to select one isoform with the highest transcript abundance for each gene. Woo et al.27 and Lau et al.28 focus on novel protein isoforms by identifying and quantifying peptides mapped to novel isoform junctions detected based on RNASeq data. While these methods are appealing, their utility in the majority of proteomic studies is limited by the prerequisite of matched RNASeq data.Leveraging RNASeq data from matched samples, the CustomProDB approach constructs a customized protein database with reduced number of protein isoforms by excluding isoforms with low transcript abundance in matched RNASeq data28 and two published human hepatocellular carcinoma (HCC) studies30. To tackle the challenge of protein isoform characterization and leverage the potential opportunities, we extend the bipartite graph representation of peptide-protein relationships17 to a tripartite graph for a comprehensive representation of the peptide, protein, and gene relationships. From the tripartite graph, we define a new quantification unit called Structurally Equivalent PEPtides (SEPEPs). These SEPEPs consist of peptide vertices that are connected to precisely the same set of protein vertices within the graph and are thus structurally equivalent in the graph. To facilitate downstream interpretation, we further divide the SEPEPs into five classes based on their patterns of connections to source proteins and genes in the tripartite graph. The introduction of SEPEPs as the quantification unit represents a significant innovation. It fundamentally differs from existing quantification approaches that employ parsimonious protein groups, individual genes, individual proteins, or correlated peptides from individual proteins, as the unit of quantification. While using peptides mapping exclusively to a single protein for quantifying that specific protein, as implemented in PQPQ and PeCorA, provides accurate quantification, it excludes many peptides that are shared by multiple proteins. On the other hand, when parsimonious protein groups or genes are employed as the quantification units, the isoform-specific information available from shotgun proteomics data is often suppressed or lost. By shifting the quantification unit to peptides that exclusively map to a group of protein isoforms that are indistinguishable based on the identified peptides, our SEPEP-based method can leverage all confidently identified peptides, including those mapping to multiple proteins or even multiple genes. Moreover, this approach retains and utilizes all the available isoform-distinguishable information present in the data, thus enhancing the detection of possible isoform regulations in our analysis. Using the iPSC and HCC datasets, we demonstrate the ability of our approach in addressing the key limitations of the parsimony-based methods, providing more comprehensive proteome characterization, identifying hundreds of isoform-level regulation events, and enabling streamlined cross-study comparisons.In this study, we systematically assess the challenge and opportunities of shotgun proteomics-based protein isoform characterization using in silico digestion data and experimental data from a published induced pluripotent stem cell (iPSC) studyAmong the 19,449 protein-coding genes annotated in the RefSeq database, 14,698 (75.6%) have more than one protein isoforms, and 3409 (17.5%) have 10 or more protein isoforms Fig. . Most isTo further assess the challenge and opportunities of isoform characterization using shotgun proteomics, we performed in silico trypsin digestion of the RefSeq protein database to generate fully tryptic peptides with length 7 to 50 and with 0, 1, or 2 missed cleavages, or with semi-tryptic peptides Fig. . Peptide28 and two human HCC studies30, one TMT-based (HCC-TMT) and one label-free (HCC-label-free). Although two missed cleavage sites were allowed in database searching, less than 5% of identified peptides had missed cleavage sites , leading to eight SEPEPs in Fig. 33 is used to estimate false discovery rate (FDR) at the SEPEP level. Specifically, a SEPEP is considered a target hit if the peptides in the SEPEP are from a forward protein sequence, and a decoy hit if the peptides are from a decoy protein sequence. SEPEPs with FDR >0.01 are excluded from further analysis . Meanwhile, no quantitative information was provided for the short isoform. In contrast, SEPepQuant reported two SEPEPs. ACTR3_SEPEP.2_C2 was associated with the three NP_005712.1-specific peptides, and it was significantly higher in tumors compared with NATs (P = 2.30e-11). ACTR3_SEPEP.1_C4 was associated with the shared peptides, and it was lower in tumors compared with NATs with a marginal significance (P = 0.02). Despite the lack of NP_001264069.1-specific peptides, it was not difficult to infer that this isoform was suppressed in tumors based on the strong elevation of NP_005712.1. Thus, SEPepQuant provided useful information for both isoforms, whereas the parsimonious inference assigned all peptides to the long isoform, leading to inaccurate quantification because some peptides may actually belong to the short isoform despite being mappable to both isoforms.rms Fig. . ParsimoFKBP11 encodes three protein isoforms, and none of the six peptides identified for this gene could be uniquely mapped to a single isoform . Although this SEPEP was associated with two CDK2 isoforms and one CDK3 isoform, it was possible to associate the decrease of this SEPEP specifically to the CDK3 isoform because another SEPEP (CDK2_SEPEP.3_C3) uniquely mapped to the two CDK2 isoforms were highly significantly overexpressed in tumors (P = 1.00e-15).In addition to providing higher resolution for characterizing multiple protein isoforms encoded by the same gene, SEPepQuant also reports quantifications for multi-gene SEPEPs (C5 SEPEPs), which may provide useful information that is typically missed in the parsimony-based methods. For example, Multiple_SEPEP.2009_C5 was associated with a peptide that could be mapped to two orm Fig. . In parsPYGL encodes two protein isoforms differing in an exon-skipping event  analysis on 20 paired tumor and NAT samples selected from the HCC-TMT study (Supplementary Dataset In order to verify the correlation between the exon-skipping event in TF) gene  and predicted proteins (XP), resulting in a total of 140,000 entries. When a SEPEP includes both curated and predicted proteins, it makes sense to focus on the curated proteins in further investigation. However, when a SEPEP includes only predicted proteins, it provides direct experimental evidence for the predictions. Opting for a more conserved protein database, such as one exclusively composed of curated proteins, may reclassify certain higher-class SEPEPs into lower classes. However, this approach could potentially overlook regulatory mechanisms involving predicted isoforms. Alternatively, when matched RNASeq data is available, the utilization of customized protein databases derived from such data represents the optimal choice. Notably, SEPepQuant can also be utilized alongside customized databases.SLK encodes a kinase that promotes apoptosis36. The pro-tumor SEPEP of SLK is a fully discriminative SEPEP and thus the pro-tumor effect could be attributed to the associated protein isoform NP_001291672.1. Compared to the longer SLK isoform NP_055535.2, this short isoform has a skipped exon, which encodes a section of a coiled-coil domain that mediates homodimerization to enhance SLK activity37. Therefore, this exon skipping may lead to reduced SLK activity and decreased apoptosis to facilitate tumor progression. Consistently, a recent analysis of RNASeq data from melanoma tumors has shown that expression of the long isoform is decreased, whereas the short isoform is increased in metastatic tumors compared with primary tumors, suggesting a role of the exon skipping in facilitating metastasis38. Transferrin is another gene found to be correlated with poor prognosis at the SEPEP level but not gene level. This pro-tumor SEPEP is also a fully discriminative SEPEP associated with NP_001054.2, the longest isoform of the transferrin gene. Transferrin is synthesized primarily in the liver and secreted into serum, with a half-life of eight days in the serum39. The unique N-terminal sequence of this isoform and the long half-life of the protein make it a promising candidate for serum biomarkers of liver cancer prognosis for further investigation.SEPepQuant identified hundreds of protein isoform-level regulatory events from both the iPSC and liver cancer datasets, highlighting widespread impact of protein isoform-level regulation in normal and disease processes. Notably, 18 genes showed significantly increased expression in liver tumors compared with normal samples as well as significant association with poor prognosis at the SEPEP level but not at the gene level. Among these, In summary, our analysis provides strong evidence to support a critical and widespread role of protein isoform regulation in normal and disease processes, and SEPepQuant is expected to have broad applications to biological and translational research to boost scientific discoveries.29 from liver cancer specimens collected from patients who underwent surgical resection at Zhongshan Hospital, Fudan University. The study was approved by the Research Ethics Committee of Zhongshan Hospital (B2017-060), and written informed consent was obtained from each patient.Protein lysates used in the PRM experiments were previously prepared40.RefSeq gene annotation and matched protein database with all NP, XP, and YP protein sequences were selected for this study (downloaded on 06/28/2020)42. The resulting peptide sequences with length <7 bp or >50 bp were excluded from our analysis.Protein sequences were cut after any K and R except followed by P using in-house script. Up to two missed cleavage sites were allowed in the in silico digestion43. Then, clustalo was applied to above multiple sequence alignments to calculate sequence similarity with percent identities as sequence distance44. Pairwise sequence similarities were extracted from the clustalo percentage identity matrix.Protein sequences for genes with multiple protein isoforms were aligned using clustalw2 with default parameters28. The HCC-TMT dataset includes 165 tumor samples from hepatocellular carcinoma patients and matched adjacent normal tissues, and the data were generated on a 11-plex TMT plagrom29. The HCC-label-free dataset includes 111 tumor samples from hepatocellular carcinoma patients and matched adjacent normal tissues, and the data were generated on a label-free platform30.Three published datasets were selected to cover different sample types and proteomics platforms. The iPSC dataset is a human cell line dataset generated on a 10-plex TMT platform. In this study, the human iPSC cells were cultured over 14 days and harvested daily for proteomics analyzinghttps://fragpipe.nesvilab.org/; V17.0) with MSFragger (v3.4) and philosopher (v4.2.1) was used for database searching and protein group-level and gene-level quantification for all three datasets15. MaxQuant (v2.3.1.0) was only applied to the iPSC dataset11. The same parameters and modifications from the original publication were used for database searching. PeptideProphet and ProteinProphet with default parameters were used for peptide validation and protein inference46. PSM count was used for label-free data quantification, and TMT ratio was used for quantification in TMT-based datasets. Median centering was used as the normalization method for TMT-based datasets.The comprehensive shotgun proteomics analysis pipeline FragPipe  previously. Filter-aided sample preparation (FASP) procedure was used for peptide preparation with all centrifugation steps at 12,000× g at 25 °C. Briefly, a total of 50 μg proteins for each sample were loaded in 10-kDa centrifugal filter tubes (Millipore), washed twice with 200 μL UA buffer , alkylated with 50 mM iodoacetamide in 200 μL UA buffer for 30 min in the darkness, washed thrice with 200 μL UA buffer again and finally washed thrice with 200 μL 50 mM NH4HCO3. Proteins were digested at 37 °C for 18 hr with trypsin (Promega) at a concentration of 1:50 (w/w) in 50 mM NH4HCO3. After digestion, peptides were eluted by centrifugation and acidated by trifluoroacetic acid (TFA) with a final concentration of 1%. Then the peptides were purified using C18 stage-tips, and the elutes were subjected to vacuum centrifugation dryness. The peptides were resolved in 20 μL 0.1% formic acid (FA) and the concentration was determined using NanoDrop.The tissues were from a published study2O and Buffer B consisted of 0.1% (v/v) FA in 80% acetonitrile. The gradient was set as follows: 1–32% B in 43 min; 32–45% B in 7 min; 45–100% B in 2 min; 100% B in 8 min. The PRM analyses were performed on a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific) using a scheduled mode to monitor both the “light” and “heavy” parent ions of the six peptides. The retention time of each peptide was determined in a preliminary experiment and the ±3 min of the retention time for each peptide was targeted for analysis. Information of the five peptides is shown in Supplementary Dataset Six standard peptides were synthesized with heavy-isotope labeled lysine in the C-terminal in GL Biochem (Shanghai) Ltd. The synthetic peptides were first resolved in 20% ACN and diluted using 0.1% FA for further use. For the PRM experiment, 1 μg peptides of each sample were mixed with the six standard peptides (1 pmol for each peptide). The peptides were separated using a home-made micro-tip C18 column (75 μm × 200 mm) packed with ReproSil-Pur C18-AQ, 3.0-μm resin  on a nanoflow HPLC Easy-nLC 1200 system (Thermo Fisher Scientific), using a 60 min LC gradient at 300 nL/min. Buffer A consisted of 0.1% (v/v) FA in Hm/z 200, AGC target 3e6, and maximum IT 50 ms by orbitrap mass analyzer (350–1500 m/z). The MS/MS scans of the target peptide were generated by HCD fragmentation at a resolution of 15,000 @ m/z 200, AGC target 1e5 and maximum IT 100 ms. Isolation window was set at 1.0 m/z. The normalized collision energy (NCE) was set at NCE 27%, and the loop count was set at 12.The MS parameters were set as follows: The spray voltage was 1800 V in positive ion mode, and the ion transfer tube temperature was set at 320 °C. MS data acquisition was performed using Xcalibur software in profile spectrum data type. The MS1 full scan was set at a resolution of 120,000 @ The MS data were processed using the Skyline software. The chromatographic peak of each peptide was manually checked. For each peptide, five product ions with the top intensities were selected for quantification analysis. The ratios of light to heavy peptides were reported and used to perform the quantification comparison among samples.This study did not employ a statistical method for predefining sample size, because all cohorts used in our investigation were derived from previously published studies. To validate our findings based on global proteomics data, we performed targeted PRM analysis on 20 liver cancer tissues. These tissues were selected based on their high or low NP_001291672.1 abundance, and protein lysate availability from the original publication. The primary focus of this study revolves around the identification and quantification of protein isoforms using datasets available in existing literature. As such, no data was excluded from the analyses, and randomization was not applicable. The Investigators were not blinded to allocation during experiments and outcome assessment.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplemental Dataset 1Supplemental Dataset 2Supplemental Dataset 3Supplemental Dataset 4Supplemental Dataset 5Supplemental Dataset 6Supplemental Dataset 7Supplemental Dataset 8Supplemental Dataset 9Supplemental Dataset 10Supplemental Dataset 11Supplemental Dataset 12Supplemental Dataset 13Supplemental Dataset 14Reporting SummarySource Data


Proteins are the structural, functional and evolutionary units of cells. On their surface, proteins are shaped into numerous depressions and protrusions that provide unique microenvironments for ligand binding and catalysis. The dynamics, size and chemical properties of these cavities are essential for a mechanistic understanding of protein function. Here, we present CaviDB, a novel database of cavities and their features in known protein structures. It integrates the results of commonly used cavity detection software with protein features derived from sequence, structural and functional analyses. Each protein in CaviDB is linked to its corresponding conformers, which also facilitates the study of conformational changes in cavities. Our initial release includes ∼927 773 distinct proteins, as well as the characterization of 36 136 869 cavities, of which 1 147 034 were predicted to be drug targets. The structural focus of CaviDB provides the ability to compare cavities and their properties from different conformational states of the protein. CaviDB not only aims to provide a comprehensive database that can be used for various aspects of drug design and discovery but also contributes to a better understanding of the fundamentals of protein structure–function relationships. With its unique approach, CaviDB represents an indispensable resource for the large community of bioinformaticians in particular and biologists in general.Database URLhttps://www.cavidb.org Proteins are the functional, structural and evolutionary units of cells. They consist of chains of amino acids that interact in complex and highly interconnected networks. On their surface, proteins are shaped into numerous cavities and protrusions that provide unique microenvironments for ligand binding or catalysis . The dynKa values based on various structural and environmental features , an interactive online database that integrates the results of commonly used cavity detection software with protein features retrieved from sequence, structural and functional analyses. CaviDB implements established cavity detection methods  of the ionizable residues and their shifts (pKa predicted − pKa expected) using PROPKA  chain or AlphaFold model was annotated via Structure Integration with Function, Taxonomy and Sequence  with ideFor the conformers’ cavities comparisons, we used the PDBSWS—PDB/UniProt Mapping . This daA responsive web interface was developed to display the data stored in a non-relational database, allowing easier navigation and visualization of the database contents on different devices. The web application was implemented in HTML, CSS, Ruby (on Rails) and JavaScript (using NodeJS).Ka shifts and the global protein descriptors  is the major protein in plasma, binds multiple ligands  and has Ka in the hydrophobic cavity of the AII binding site, which creates a microenvironment favorable for catalysis  containing residues Arg410 and Tyr411, previously described as part of the catalytic active site, was also identified  Figure . In addihttps://alphafold.ebi.ac.uk/). Recently, AlphaFold models were found to correctly predict some of the native conformations of protein ensembles    and provIdentification of binding cavities is critical for understanding the relationship between protein structure and function and is a crucial step for drug design , 61, 62.baad010_SuppClick here for additional data file.