ProtTucker, has an improved ability to recognize distant homologous relationships than more traditional techniques such as threading or fold recognition. Thus, these embeddings have allowed sequence comparison to step into the ‘midnight zone’ of protein similarity, i.e. the region in which distantly related sequences have a seemingly random pairwise sequence similarity. The novelty of this work is in the particular combination of tools and sampling techniques that ascertained good performance comparable or better to existing state-of-the-art sequence comparison methods. Additionally, since this method does not need to generate alignments it is also orders of magnitudes faster. The code is available at https://github.com/Rostlab/EAT.Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any annotation. A recent alternative expands the concept of HBI from sequence-distance lookup to embedding-based annotation transfer (EAT). These embeddings are derived from protein Language Models (pLMs). Here, we introduce using single protein representations from pLMs for contrastive learning. This learning procedure creates a new set of embeddings that optimizes constraints captured by hierarchical classifications of protein 3D structures defined by the CATH resource. The approach, dubbed  Protein sequence determines structure which determines function. This simple chain underlies the success of grouping proteins into families from sequence . Informadaylight zone at HVAL > 5  over 90% of all protein pairs have similar 3D structures, while at the beginning of the midnight zone for HVAL<-5 , over 90% have different 3D structures. Thus, the transition from daylight to midnight zone is described by a phase-transition in which over about ten percentage points in PIDE precision drops from 90% to 10%, i.e. from almost all correct to almost all incorrect within ±5 points PIDE. The particular point at which the twilight zone begins and how extreme the transition is, depends on the phenotype: steeper at lower PIDE for structure  at the expense of a decrease in precision (relations identified correctly) over a narrow range of sequence similarity. Measuring sequence similarity by the HSSP-value (HVAL) ,12 for ttructure ,14.midnight zone, i.e. have seemingly random sequence similarity . Using such information as input to machine learning methods has been generating essentially all state-of-the-art (SOTA) prediction methods for almost three decades  such as ELMo  equating amino acids with words in NLP and the sequence of entire proteins with sentences. Such pLMs learn to predict masked or missing amino acids using large databases of raw protein sequences as input . Embeddas input  or even as input ,46,47 thas input ,49. The as input . The recEmbeddings from pLMs capture a diversity of higher-level features of proteins, including various aspects of protein function and structure ,49,50,51Supervised learning directly maps the input to the class output. Instead, contrastive learning , optimizProtTucker and its embeddings were established to identify more distant relations than is possible from sequence alone. One important objective of ProtTucker is to study entire functional modules through identifying more distant relations, as found to be crucial for capturing mimicry and hijacking of SARS-CoV-2  for early stopping were randomly split off from CATH-S100 while ensuring that (1) every homologous superfamily appeared maximally once in test300 ∩ val200 and (2) each protein in test300 & val200 has a so called Structural Sub-group (SSG) annotation, i.e. clusters of domain structure relatives that superpose within 5Å (0.5 nm), in CATH. To create the training set, we removed any protein from CATH-S100 that shared more than 20% pairwise sequence identity (PIDE) to any validation or test protein according to MMSeqs2 (–num-iterations 3) with highest sensitivity (-s 7.5) and bidirectional coverage (–cov-mode 0). Additionally, large families (>100 members) within CATH-S100 were clustered at 95% PIDE and length coverage of 95% of both proteins using MMSeqs2 . The cluster representatives were used for training  and as lookup set during early stopping on set val200. We needed a lookup set from which to transfer annotations because contrastive learning outputs embeddings instead of class predictions. For the final evaluation on test300, we created another lookup set but ignored val200 proteins during redundancy reduction . This provided a set of proteins for validation which had similar sequence properties to those during the final evaluation while ‘hiding’ them during training and not using them for any other optimization. To ensure strict non-redundancy between lookup69k and test300, we further removed any protein from test300 with HVAL > 0 . All performance measures were computed using test219.The sequence-unique datasets provided by CATH ,54 v4.3  MMSeqs2  applying–cov-mode 0) and then applied the same MMSeqs2 profile-search (–num-iterations 3 –s 7.5) as outlined above to remove cluster representatives with ≥ 20% PIDE to any protein in test300 or val200  ≤ 20%). This filtering yielded 11M sequences for an alternative training set (dubbed train11M).Data augmentation can be crucial for contrastive learning to reach performance in other fields . HoweverThe CATH detection of ProtTucker was further analyzed using a strictly non-redundant, high-quality dataset. This set was created by first clustering CATH v4.3 at 30% using HMM profiles from HMMER and additionally discarding all proteins without equivalent entry in SCOPe, i.e. the domain boundaries and the domain-superfamily assignment had to be nearly identical . We used the highly sensitive structural alignment scoring tool SSAP ,66 to coDeepLocTrain set to 490 proteins in a recently proposed test set (setHard) that was strictly non-redundant to DeepLocTrain. Datasets described elsewhere in more detail , Armill, TrEMBL ) and Megd TrEMBL ).pLM): (–) ProtBERT (ESM-1b (ProtT5-XL-U50 (ProtT5 for simplicity) based on the NLP sequence-to-sequence model T5 (ProSE (ProSE-DLM) or on additionally predicting intra-residue contacts and structural similarity from 28k SCOPe proteins (ProSE-MT). While ProSE, ProtBert and ESM-1b were trained on reconstructing corrupted tokens/amino acids from non-corrupted (protein) sequence context (masked language modeling), ProtT5 was trained by teacher forcing, i.e. input and targets were fed to the model with inputs being corrupted protein sequences and targets being identical to inputs but shifted to the right (span generation with span size of 1 for ProtT5). Except for ProSE-MT, all pLMs were optimized only through self-supervised learning exclusively using unlabeled sequences for pre-training.Protein sequences were encoded through distributed vector representations (embeddings) derived from four different pre-trained protein language models (ProtBERT  based onProtBERT  but traiProtBERT . (2) ESM (ESM-1b  is simil (ESM-1b . (3) Pro5-XL-U50  . From this L × N embedding matrix, we derived a fixed-size N-dimensional vector representing each protein by averaging over protein length . The pLh Figure , for Protanh) as non-linearity between layers. We also experimented with deeper/more sophisticated networks without any gain from more free parameters (data not shown). This confirmed previous findings that simple networks suffice when inputting advanced embeddings  projected fixed-size per-protein (sentence-level) embed-dings from 1024-d (1280-d/6165-d for ESM-1b and ProSE respectively) to 256 and further to 128 dimensions with the standard hyperbolic tangent  describing the increasing structural overlap between triplets was picked. This defined a positive (same CATH-number as anchor up to level α’≤α) and a negative label (same CATH-number as anchor up to level α’< α). For instance, assume the anchor has the CATH-label 1.25.10.60  and we randomly picked α = 3 (topology-level), only proteins with the anchor's topology  qualify as positives while all negatives share the anchor's architecture  with different topology (y≠10). Self-hits of the anchor were excluded. From the training proteins fulfilling those constraints, one positive and one negative were picked at random. If no triplets could be formed (e.g. α = 4 with a single-member homologous superfamily had no positive for this anchor/α combination), α was changed at random until a valid triplet could be formed . This flexibility in sampling enabled training on all proteins, independent of family size.During training, the new embedding space spanned by the output of the FNN was optimized to capture structural similarity using triplets of protein embeddings. Each triplet had an anchor, a positive and a negative. In each epoch, all daylight zone). As the final triplet selection was still random, anchor-positive pairs could still be too easy/similar which was shown to hinder the success of contrastive learning  while increasing the rate of semi-hard anchor-positive/anchor-negative pairs. Assume multiple proteins of the topology Leucine-rich Repeat Variant were within one mini-batch, the hardest positive for each anchor would be picked by choosing the anchor-positive pair with the largest Euclidean distance. Accordingly, anchor-negative pairs would be picked based on the smallest Euclidean distance. For each mini-batch, this sampling was applied to all four levels of the CATH-hierarchy, so triplets were re-wired on all four CATH levels resulting in a total batch-size of about: batch_size * 3 * 4. This was an ‘about’ instead of ‘equal’ because for some mini-batches, not all proteins had valid triplets for all four levels.Unlike randomly sampling negatives, the hierarchical sampling could be described as semi-hard sampling as it zoomed into triplets that were neither too easy  nor too hard (outliers) to classify by ensuring a minimal overlap between the anchor and the chosen negative/positive pair. Thereby, trivial triplets are under-sampled (avoided), i.e. those with 3D structures so different that the separation becomes trivial (learning . To solvsampling  which ofSoft Margin Loss was used to optimize this new embedding space such that anchor-positive pairs were pulled together (reduction of Euclidean distance) while pushing apart anchor-negative pairs (increase of Euclidean distance). The efficiency of combining the Soft Margin Loss with batch-hard sampling was established before  to project the 1024-d (or 1280-d/6165-d for ESM-1b or ProSE respectively) embeddings of all proteins, irrespective of anchor, positive or negative, to a new 128-d vector space. The d before , althougB represents one mini-batch created through hierarchical sampling, f(a), f(p) and f(n) represent the ProtTucker embeddings of proteins a (anchor), n (negative), and p (positive) represented as pLM embeddings; iC represents the CATH annotation of a protein on the i’th hierarchy level of CATH; finally, D(f(a),f(x)) represents the Euclidean distance between the embeddings for proteins a and x. We created the mini-batch B used for training by choosing for each protein or anchor a in B the hardest negative n and the hardest positive p by picking those proteins in B that have the smallest | largest Euclidean distance D to a ProtTucker embedding space while not sharing | sharing iC, respectively. Those semi-hard triplets are indexed by t and d[t] referring to the difference between D of anchor-negative and D of anchor-positive. In our case, the label for the t’th triplet y[t] is always 1 as the sign of x indicates training success, i.e. whether the distance anchor-positive is smaller than that between anchor-negative.Adam optimizer (early stopping) at the highest accuracy in predicting the correct homologous superfamily for setval200.Consequently, triplets of varying structural similarity were used simultaneously to optimize a single, shared network, i.e. all four CATH-levels were learned by the same network at the same time Figure . We usedptimizer  with a ltest219 with the lookup69k as lookup set  served as final evaluation. If no protein in the lookup set shared the annotation of the query protein at a certain CATH-level (more likely for H than for C), the sample was excluded from the evaluation of this CATH-level as no correct prediction was possible  were generated as for homology-based inference (HBI), i.e. given protein X with experimental annotation (CATH assignment) and a query protein Q without, then HBI transfers the annotation from X to Q if sequence similarity SIM> threshold. For contrastive learning, we replaced SIM by D(f(X),f(Q)) with D as the shortest Euclidean distance in embedding space Figure . In prevpossible .E-value of 10.During evaluation, we compared the performance of our embedding-based annotation transfer (EAT) to HBI using the sequence comparisons from MMSeqs2 . While tlookup69k to test219.For both approaches, EAT and HBI, we computed the accuracy as the fraction of correct hits for each CATH-level. A hit at lower CATH-levels could be correct if and only if all previous levels were correctly predicted. Due to varying number of samples at different CATH-levels , performpositive class, 2) TN (true negatives): correctly predicted to be in the negative class, 3) FP : incorrectly predicted to be positives, and 4) FN : incorrectly predicted to be in in the negative class. Here, we focused on performance measures applicable for multiclass problems and are implemented in scikit  are defined by their secondary structure content. These four branch into 5481 different superfamilies with distinct structural and functional aspects CATH v4.3.0). However, most standard metrics are defined for binary cases which requires some grouping of predictions into four cases: 1) TP (true positives): correctly predicted to be in the n scikit . These w.0. Howevk truly occurred, k was predicted, In these definitions accuracy corresponds to precision, and coverage to recall binarizing a multiclass problem through micro-averaging, i.e. by counting the total TPs, FPs and FNs globally, irrespective of the class. The multi-class extension of Matthew's correlation coefficient  was defn = 1000 bootstrap sets; for each bootstrap set we randomly sampled predictions from the original test set with replacement. Standard deviation (or in the case of bootstrapping: bootstrap standard error) was calculated as the difference of each test set  and the annotation of the best hit, e.g. measured as lowest E-value, is transferred if it is below a certain threshold <10–3). This relates to inferring the annotation of a query protein from the k-Nearest Neighbors (k-NN) in sequence space. More recently, similar approaches expanded from distance in sequence to distance in embedding space . We used EAT as a proxy for the comparison of embeddings from five different pLMs: ProSE-DLM & ProSE-MT .Homology-based inference (HBI) uses sequence similarity to transfer annotations from experimentally characterized (labelled) to uncharacterized (unlabeled) proteins. More specifically, an unlabeled query protein Q is aligned against a set of proteins X with experimental annotations  outperformed random annotation transfer  were separated through training: the distribution of all pairwise Euclidean distances within (intra/same) and between (inter/different) superfamilies in g Figure . Before g Figure : red andg Figure : blue ovtrain66k) and the parameters for dimensionality reduction were identical. T-SNE projections of raw ProtT5 embeddings qualitatively suggested some class separation (clustering). The information underlying this separation was preserved when projecting ProtT5 embeddings through an untrained ProtTucker : 10.1M), 7.1M had to be discarded due to low quality (SSAP-score < 50), leaving 2.9M pairs of which only 1.8% (53k pairs) had the same homologous superfamily  with the structural similarity of those pairs computed via SSAP ,66 Figu. From thy Figure : blue. Dy Figure  already y Figure . This waX)—in this section also shortened to PT(X)—to refer to the embeddings output by inputting the pre-trained pLM X into the contrastive learning. The improvements were larger for more fine-grained CATH levels: all models improved significantly for the H-level, while only PT(ProtBERT) and PT(ESM-1b) improved from 4 to 14 or from 0 to 21 percentage points for the C-, and the H-level, respectively. PT(ProtT5) consistently outperformed all other pLMs on all four CATH-levels, with an increasing performance gap toward the more fine-grained H-level at which all pLMs except for PT(ESM-1b) performed significantly worse. The improvements from contrastive learning for PT(ProSE-DLM) and PT(ProSE-MT) were mostly consistent but largely insignificant. Especially, the model already optimized using labeled data (ProSE-MT) hardly improved through another round of supervision by contrastive learning and even worsened slightly at the H-level.The trend captured by the better separation of distributions Figure  and stru3) to 11m (11 × 106) proteins (15-fold increase) and raised performance, although the higher values were neither statistically significant nor consistent (Table We augmented the training set for PT(ProtT5) by adding HBI-hits from HMM-profiles provided by CATH-Gene3D . This increased the training set from 66k 66 × 1003 to 11mbatch-hard and hierarchical sampling on performance by removing each component when training PT(ProtT5)  Table . BenchmaE-value . In analogy, we explored the corresponding relation for EAT, namely the correlation between accuracy . Here, we compared the effect of gradually removing hits depending on their E-values. Essentially, this approach measured how sensitive performance was to the degree of redundancy reduction between query and lookup set. For instance, at a value of 10–3  and template . Usually, the significance of an inference is measured as the chance of finding a hit at random for a given database size -using them as exclusive input to predict subcellular location in ten states. To this end, we EAT transferred annotations from an established data set  to a strtrain66k. Nevertheless, when splitting test219 into three bins of varying family sizes, we still observed a trend towards higher accuracy (Equation By clustering very large protein families (>100 members after redundancy reduction) at 95% PIDE, we constrained the redundancy in set Equation  for largEquation . We chostrain66k and analyzed the five pairs (10 proteins) with the highest Euclidean distance in the same homologous superfamily  to any other protein within this family . In contrast, the nearest neighbor  linked e kinase ) of 3skjA2_HUMAN ) with thA2_HUMAN ). Such atrain66k. This took 23m and 11m, respectively. Embeddings were generated using ProtT5 in half-precision with batch processing. All times were measured on a single Nvidia RTX A6000 with 48GB of vRAM and an AMD EPYC ROME 7352.Training ProtTucker(ProtT5) required generating ProtT5 embeddings for labeled lookup proteins from which annotations can be transferred to unlabeled query proteins. Embeddings for this lookup set are pre-computed for the first query and can be re-used for all subsequent queries at any future time. The time required to labeled lookup proteins from which annotations can be transferred to unlabeled query proteins. Embeddings for this lookup set are pre-computed for the first query and can be re-used for all subsequent queries at any future time. The time required to generate ProtTucker embeddings from the embeddings of pLMs was negligible as its generation required only a single forward pass through a two-layer FNN. This implied that the total time for EAT with ProtTucker was largely determined by the embedding generation speed. For instance, creating per-protein embeddings from ProtT5 for the 123k proteins in CATH-S100 required 23 min (m). The total time for creating ProtTucker(ProtT5) embeddings from ProtT5 embeddings for the same set on the same machine was 0.5 seconds (s), i.e. ProtTucker added about 0.3%. Creating HMM profiles for the same set using either MSAs from MMSeqs2  or jackhmmer took 15 m or 30 h, respectively , pre-computed HMM profiles (HMMER) or pre-computed embeddings (ProtTucker) to transfer annotations from CATH-S100 to a single query protein took on average 0.025, 1.5 or 0.0008 s, respectively. Proteins in the PDB and CATH are, on average, roughly half as long (173 residues) as those from UniProt (343 residues). This is relevant for runtime, because embedding generation scales quadratically with sequence length .E-value < 10–3. For the subset of proteins for which both methods transferred annotations, those largely agreed .This increase was also reflected for the proteome-wide annotation transfer Table , althougshortcut is to learn each level in the hierarchy independently (We have presented a new solution for combining the information implicitly contained in the embeddings from protein Language Models (pLMs) and contrastive learning to learn directly from hierarchically sorted data. As proof-of-concept, we applied the concept to the CATH hierarchy of protein structures ,1,6,80. endently  at the pendently  to 10,00endently . Althougendently , Table 1endently , Table 1semi-hard triplets that are neither too simple nor too hard to learn  and it increased the number of possible triplets manifold compared to only sampling on the level of superfamilies. These advantages might partially explain the synergy of both sampling techniques , we used embedding based annotation transfer  and one of the largest viruses . In <27 min on a single machine  the annotations agreed to over 80% with Gene3D, while this value dropped to 75% for the fungus , possibly suggesting that ProtTucker using averages over an entire protein for comparison did not trip substantially more over the multi-domain challenge than the local alignment-based Gene3D using HMMER ) was similar to HBI using HMM-profiles on the most fine-grained level of homologous superfamilies .Taken together, these results indicated that contrastive learning captured structural hierarchies and provides a novel, powerful tool to uncover structural similarities clearly beyond what has been achievable with 50 years of optimizing sequence-based alignment techniques. Using EAT to complement HBI could become crucial for a variety of applications, ranging from finding remote structural templates for protein 3D structure predictions over prioritizing new proteins without any similarity to an existing structure to filtering potentially wrong annotations. One particular example has recently been shown for the proteome of SARS-CoV-2 to unravel entire functional components possibly relevant for fighting COVID-19 .Previously , we showonline learning): While supervised networks require re-training to benefit from new data, contrastively trained networks can benefit from new data by simply updating the lookup set. This could even add completely new classes, such as proteins for which the classification will become available only in the future. HBI shares this advantage that originates from the difference between classifying proteins into existing families versus classifying by identifying the most similar proteins in that family. (ii) Learn the access, not the data: Instead of forcing the supervised network to memorize the training data, contrastive learning teaches how to access the data stored in an external lookup set. (iii) Compression: As many other learning techniques, contrastive learning can act as a compression technique. For instance, we reduced the disk space required to store protein embeddings threefold by projecting 1024-dimensional vectors onto 128 dimensions while improving performance  Dynamic data update (Embeddings from protein Language Models (pLMs) extract the information learned by these models from unlabeled protein sequences. Embedding-based Annotation Transfer (EAT) replacing the proximity in sequence space used by homolog-based inference (HBI) through proximity in embedding space already reaches traditional alignment methods in transferring CATH annotations from a template protein with experimental annotations to an unlabeled query protein. Although not quite reaching the performance of advanced profile-profile searches by HMMer for all four CATH levels, the best embeddings surpassed HMMer for two of the four levels (C and A). When optimizing embeddings through contrastive learning for the goal of transferring CATH annotations, EAT using these new embeddings consistently outperformed all sequence comparison techniques tested. This higher performance was reached at a fraction (three orders of magnitude) of the computational time. Although the new embeddings optimized through contrastive learning for CATH did not improve performance for a completely different task, namely the prediction of subcellular location in ten classes, the CATH-optimized solution did also not perform significantly worse. Remarkably, just like HBI, the performance of EAT using the optimized ProtTucker embeddings was proportional to family size with increased accuracy for larger families.https://github.com/Rostlab/EAT.Building on top of bio_embeddings package  we have lqac043_Supplemental_FileClick here for additional data file.


Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength. Proteinechanism . Concernechanism . Two recechanism . Similarechanism . Secondlechanism .dK or the Gibbs free energy difference b = -RT ln Kd) . ExperimT ln Kd) . From thT ln Kd) . On the T ln Kd) , are widT ln Kd) . This, iT ln Kd) , such asT ln Kd) . HoweverIn this context, water molecules also play a crucial role in the process of binding, in specific recognition and in protein-protein interactions . In factIn this study, we aim to continue this effort to characterize the dynamics of protein-protein complexes and their interfaces at molecular level. To get insights on the dynamics of protein-protein complexes, we study a set of eight transient protein-protein complexes for which high resolution crystallographic 3D structures are available and thermodynamic parameters are reported in the Affinity Benchmark . We perf2) and experimental binding affinities . To model our starting structures, we verified that there were no mutations in the unbound form with respect to the bound complex, no gap in the backbone and we used the same number of residues for the complex and the free proteins. Terminal residues present in the free proteins but absent from the complex were chopped, and residues present in the complex but absent in the free proteins were modeled based on the complex. We computed a pKa calculation on the complex structure using the software PDB2PQR (Eight binary protein-protein complexes from the Docking benchmark  and the  PDB2PQR  and the  PDB2PQR  to defin+Cl− ion pairs (T = 1 ps) and Berendsen pressure coupling (tP = 4 ps) (τT = 1 ps) and a Parrinello-Rahman barostat (τP = 1 ps) (All-atom Molecular dynamics (MD) simulations were performed with the GROMACS five package  using thon pairs  were addon pairs  with a ron pairs , allowinon pairs . Translaon pairs . After e = 4 ps)  by slowi = 4 ps)  (τT = 1 = 1 ps)  for 10 nWe computed the clustering analysis based on backbone RMSD on the complexes and the single proteins using the algorithm TTClust . For theFor RMSF calculations, we first superimposed the trajectory on the starting structure for the unbound proteins and on the separated chains for complexes. We computed RMSF only for the backbone atoms and we averaged the fluctuations for each residue. This procedure allowed us to study the change of flexibility upon binding for each chain.The interface analysis is conducted on MD snapshots taken every ns . Interface RMSD (iRMSD) between different snapshot was computed with DockQ ..Interface contacts are defined at the residue level using a 5 Å distance cutoff between heavy atoms. The solvent accessible surface area (ASA) is computed with the NACCESS software using the default radius of 1.4 Å to take into account the water molecule .rim if relative ASA in the complex is greater than 25%, core if relative ASA in the isolated chain is greater than 25% and lower than 25% in the complex.We define distinct regions at the interface according to the criteria introduced by Levy . ResidueA and ASAB denote the accessible surface areas of separate chains and ASAAB the accessible surface area of the complex.The area of accessible surface buried by the interface is defined by:The gap index is defined assoftware .gmx hbond routine implemented in GROMACS , and which have similar frequencies (low variance).XY) is the variance of the relative frequency of the XY contact across clusters. For example, a residue X seen in contact with residue Y with a variance of 0.1 and in with residue Z with a variance of 0.3 will receive a value of 0.3.Variance values refer to contacts between residue pairs. To map these variance values at the level of residues, we selected, for each residue, the maximum variance observed across contacts:XY) is the relative frequency of contact between X and Y. The inner maximum is taken over interacting residues in each cluster, and the outer minimum is taken over clusters. For example, if a residue X is seen at 100% in contact with residue Y and 4% with residue Z in one cluster, and 10% in contact with residue Y and 60% with residue W, its recurrence index is equal to 60%. This allows discriminating between residues that are part of the interface in every cluster (high recurrence index) and those that come and go (low recurrence index), without being affected by the unbalance between clusters.We also derived a recurrence index, to quantify the recurrence of each residue in contacts across clusters. It is defined by:Free energy changes associated with mutations in the complexes under study were extracted from SKEMPI 2.0 . MultiplGnp,disp, solv) between the protein and the water molecules, we took N snapshots from the MD simulations separated by 1 ns, we minimized them by turning off all electrostatics interactions and we ran 50 ps using the ensemble NVT. Then, we determined the dispersion energy between the protein(s) and the water for each snapshot and we averaged it and used it as the repulsive part of the nonpolar solvation energy for the macromolecule.In order to compute the nonpolar solvation dispersion energy (∆dm) of the water O and the heavy atoms of the protein  and the oxygen of the water molecules as for determining the number of water molecules released. In this manuscript we referred to these water molecules as interfacial water molecules. Interfacial water molecules were determined for the representative structures and along MD simulations. For the latter, we also analyzed the contacts formed for each interfacial water molecule with the same distance cutoff and the two protein partners to investigate the contacts mediated by water.The correlation between variables was assessed using the Pearson correlation coefficient. The difference between two correlation coefficients was assessed using the Dunn and Clark test  implemenAs presented in the Section Material and Methods, we performed clustering analysis based on backbone RMSD on the complexes and the single proteins and we computed the RMSD time series. RMSD time series and clustering results are summarized in Interestingly, proteins interact in a similar way in these two complexes: in 1AK4, a loop of the HIV capsid is inserted in the cyclophilin binding site and in 3SGB, a loop of the inhibitor is inserted in the active site of the protease see . In bothTo understand the impact of the dynamics on the geometric parameters, we computed the ∆ASA, the gap volume and the gap index on the X-ray complexes and along MD trajectories. Distributions are shown in t = 0) and the average value obtained along the trajectory. All geometric interface parameters show variability during the simulation. Even though the initial values can differ from the crystallographic ones, the simulations sample values around the crystallographic one in most of the cases  has a very stable interface, with about 90% of initial contacts preserved during the 300 ns of simulation. The SH3 domain complex 1GCQ  also has a very stable interface, with about 80% of initial contacts preserved. In all other complexes , the fraction of initial contacts frequently drops under 80%, and as low as 50% for complex 1PVH (Interleukine six receptor/leukemia inhibitory factor). Interestingly, some complexes display distinct conservation levels during the simulation, suggesting the existence of distinct interface substates, like the barnase/barstar complex 1BRS, for which the fraction of initial contacts fluctuates around 90% during the first 125 ns of simulation and then around 75% for the rest of the simulation. The existence of distinct substates at the interface is also suggested by the change in the absolute number of interface contacts along MD trajectories , see via the Jaccard index J based on interface contacts and performed hierarchical clustering, as explained in the Material and Methods section. This analysis revealed the existence of distinct substates at the interfaces. Clustering results are shown in 2 has 4 clusters, whereas complex 1BRS (barnase/barstar) with a large interface  has only two clusters, even when the simulation is extended up to 500 ns (data not shown). In the next section, we further explore the structural differences between substates.To investigate the existence of substates at the interface, we computed the similarity between interfaces along the trajectory To highlight the contacts that are responsible for the differences between clusters, we computed the variance of the relative frequencies of interface contacts. The same analysis was also carried out for the other five complexes with more than one cluster. In the majority of them, a few contacts, involving two to six residues per protein, are found responsible for the difference between clusters, see In this section, we characterize the different interface substates visited along MD simulations in the different systems. For this, we considered interface properties and structure representatives in each cluster. Average properties computed for each interface cluster are reported in To analyze the structural differences between interfaces clusters, we extracted representative structures as explained in the Material and Methods section. For each molecular system, we superimposed the representative structures of the different clusters to highlight global changes. We also visualized the interfaces and made the distinction between 1) interface residues that preserve the same contacts in the different clusters, 2) interface residues that make different contacts and 3) interface residues that are specific to some clusters. We also included interface waters in the visualization.2 in cluster one versus 1,459 Å2 in cluster 2). As binding affinity is linked to the interface size, the two clusters presumably have different binding affinities. The existence of substates with lower affinity could assist in the reversibility of the protein-protein interaction.The representative structures in the case of the barnase/barstar complex 1BRS is shown in Representative structures for substates in other complexes are shown in As shown earlier, the cyclophilin/HIV capsid complex 1AK4, undergoes a significant reorientation of the two proteins during the simulation time, which can be seen on the structure representatives see . The strWe compared our results on interface contacts dynamics with available data for binding affinity changes upon mutations from the SKEMPI v2.0 database . Four ofTo better characterize the interfaces and the complexes, we also analyzed the role of water from an energetic and a structural point of view at the interface and in the formation of the complex.Gnp,disp, solv, and the number of water released at the first and second shell, as explained in the Material and Methods section. Then, we analyzed the relationship between the number of water molecules released at the first shell and ∆ASA and the binding affinity. via linear equation with respect to ASA of the protein and protein complexes, it turned out that there is no simple relationship between ∆ASA and the dispersive solvation energy, ∆Gnp,disp, solv, see We computed the dispersive solvation energy, ∆First, for each representative structure we determined the number of interfacial waters and we observed that there are no significant changes along the clusters, unlike the other interface descriptors see . In factWe analyzed the contacts between interface residues and interface water molecules and confronted those results with the interface clustering. To better characterize the contacts with water and their impact on the interactions, we also analyzed the contacts that are mediated by water molecules at the interface, i.e., triplets constituted by a residue from one chain in contact with a water molecule which is in contact with a residue from the other chain. A water-mediated contact between two residues is then defined by the existence of such triplet in the snapshots. The connecting water molecule can change between snapshots since its dynamics is much faster than the time between each snapshot. Since the study of the dynamics of water is beyond this work, we only assess the stability of the residue pairs. We computed the relative frequency of such water-mediated contacts in each complex, also taking into account the location of the residues in the different regions of the interfaces (core or periphery). These results are shown in Protein-protein interfaces can no longer be considered as static objects, but their dynamical nature has to be taken into account to give a more complete picture of protein-protein complexes. The present work aimed at describing what is the dynamical fate of protein-protein interfaces. We observed that at the scale of hundreds of ns, most protein-protein complexes, beyond a certain interface size, visit distinct interface substates. Interface substates are characterized by alternate interface contacts, direct or mediated by water molecules, with interface waters accompanying these rearrangements and modulating the hydration states of residues. It is important to note that these changes are not confined to the periphery of the interfaces, but also affects residues of the interface core. Either geometric or physio-chemical interface properties between substates are significantly affected in a case-dependent fashion, which could result in different complex stability and help the interaction reversibility.Our work can be put in relation to other studies of protein-protein complexes by MD. Simulations of the association process of protein-protein complexes at the sub-millisecond scale by In a recent work, Our work also highlights the crucial role of water in the binding and in the strength of the interactions, in terms of the number of released water molecules and the number of contacts mediated by water molecules at the interface. In this context, we observed that the number of released water molecules at the first shell is strongly correlated to the experimental binding affinity. Hence, first, our results suggest that for MM-PBSA approaches it is recommended to take into account the simulations of the unbound and bound states (3 simulations). Secondly, a new way to describe the non-polar solvation energy seems crucial in MM-PBSA approaches and in protein-protein docking based on physical scores. Finally, the amount of water-mediated contacts observed in the simulations of the complexes suggests that in protein-protein docking the presence of water molecules at the interface may open a new route to predict them.


Accurate and rapid prediction of protein–ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein–ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications.https://github.com/lennylv/CAPLA.The source code of the method along with trained models is freely available at Bioinformatics online. The interactions between proteins and small molecule ligands involve in a variety of biological processes, including enzymatic catalysis and signaling transduction. Furthermore, drugs usually act as ligands and interact with target proteins to exert their effects in drug discovery , for improved prediction of binding affinity using sequence-level information of both protein and ligand only. More specifically, CAPLA contains three inputs, namely the protein and pocket input representations that comprise amino acid types, secondary structure elements (SSEs) and residue physicochemical properties, as well as the ligand SMILES strings. Then, CAPLA leverages the cross-attention mechanism to capture the mutual interaction features between pockets and ligands, and then adopts the dilated convolutions to learn multiscale long-range features for proteins, pockets and ligands, respectively. Finally, the learned feature vectors of proteins, pockets and ligands are concatenated as the input of a fully connected neural network (FNN) to predict the protein–ligand binding affinity. Experiments of protein–ligand binding affinity prediction on two test datasets demonstrated that CAPLA achieves improved performance with comparisons to several state-of-the-art baseline methods. In addition, we also conducted two external independent tests for CAPLA, and the results demonstrated its superior generalization capability. Moreover, we provided the interpretability for CAPLA by analyzing the attention score in each residue within protein-binding pocket sequences, and found that our method can capture critical functional residues that are important contribution to the protein–ligand binding. Based on these results, it can be concluded that CAPLA is an effective approach for protein–ligand affinity prediction and may contribute to useful guidance for further drug development.The commonly used dataset of protein–ligand binding affinity was derived from the PDBbind database of version 2016  between To evaluate the generalization capability of our method, we also employed the CSAR-HiQ dataset  is optimized by the AdamW  loss function to train our model, which is formally defined as follows.he AdamW  optimizaIn CAPLA, the convolution and FC layers adopt PReLU  as activThe CAPLA model is implemented by CUDA 11.3 with PyTorch 1.10.2, and runs on GTX1080Ti GPU. More details about the experimental environment for the implementation of CAPLA were provided in R), root mean square error (RMSE), mean absolute error (MAE), standard deviation (SD) and concordance index (CI). The definition of these five metrics is provided in In this work, to evaluate the performance of our method and compare with other baseline methods, we adopted the widely used evaluation metrics in the protein–ligand binding affinity prediction task, including Pearson correlation coefficient  are displayed in In order to demonstrate the generalization capability of CAPLA, we performed two additional tests on two external datasets from independent sources (e.g. industry and academic laboratories), i.e. CSAR-HIQ_51 and CSAR-HIQ_36. We also compared the performance of CAPLA to those of DeepDTAF, Pafnucy, IGN and IMCP-SF, whose values of all evaluation metrics were computed from their best models.As mentioned before, the CSAR database does not directly provide the protein-binding pockets, but the CAPLA model leverages the pocket as input, and hence we need to extract the residues within a certain radius of the ligand as the pocket from the PDB files of complexes in the CSAR database. In order to identify an optimal ligand radius, we established five different radiuses  and evaluated their respective impacts on the prediction performance in the combination of CSAR-HIQ_51 and CSAR-HIQ_36 sets. As displayed in R values of 0.686 and 0.704, respectively. These results manifest that CAPLA is capable of generalizing well to different independent data.The prediction performance of CAPLA on CSAR-HIQ_51 and CSAR-HIQ_36 sets is shown in R and RMSE over DeepDTAF by 8.0% and 18.6%, over Pafnucy by 6.4% and 4.9% and over IGN by 25.9% and 18.3%, respectively. However, our method performs worse than IMCP-SF on this test set. For the CSAQ-HIQ_36 test set, CAPLA achieves considerable improvement in terms of R and RMSE, i.e. over DeepDTAF by 16.1% and 47.4%, over Pafnucy by 13.8% and 12.3%, over IGN by 17.6% and 19.0% and over IMCP-SF by 7.3% and 6.7%, respectively. These comparison results indicate that the generalization capability of our method is significantly superior to those of other state-of-the-art baseline methods on different independent data.The results of comparison of CAPLA with DeepDTAF, Pafnucy, IGN and IMCP-SF on CSAQ-HIQ_51 and CSAQ-HIQ_36 test sets are displayed in In conclusion, the benchmark tests and the independent source tests demonstrate the superiority of CAPLA over other state-of-the-art baseline methods in protein–ligand binding affinity prediction. We consider that the superior prediction capability of CAPLA may benefit from the use of cross-attention mechanism, which can help facilitate to capture the mutual interaction features between protein-binding pockets and ligands.The aforementioned results demonstrated the superiority of our method CAPLA in protein–ligand binding affinity prediction. In the following, we attempted to explore the black-box mechanism of how the model makes accurate binding affinity prediction and provide an interpretable analysis for CAPLA through attention scores of protein-binding pocket sequences and ligand SMILES.Visualizing the features learned by cross-attention mechanism of CAPLA. In order to acquire an intuitive understanding of the features learned by CAPLA, we applied the t-Distributed Stochastic Neighbor Embedding (t-SNE) tool to map the high-dimensional feature representations (213D) extracted respectively after the embedding layer, the cross-attention layer and the convolution layer onto the 2D space as visualized in Case studies for matching experimental results. To further illustrate the underlying knowledge learned by CAPLA, we visualized the attention scores of all residues in the protein-binding pocket and analyzed the key functional residues that contribute significantly to the prediction of protein–ligand binding affinity. Here, we employed the cross-attention mechanism of CAPLA to generate the attention scores for the pockets in three protein–ligand complexes  from Test2016_260. Specifically, we first extracted the two cross-attention score matrices (Q refers to the embedding representation of a ligand SMILES string, and the key matrix K refers to the embedding representation of a pocket sequence. Then, we generated two position score vectors (Furthermore, we explored the universal patterns detected by CAPLA in protein–ligand interaction. The attention scores of the amino acid types of all pocket sequences in Test2016_290 by Head 1 and Head 2 in the cross-attention mechanism are shown in The findings of these functional residues may be capable of uncovering the underlying mechanism of the protein–ligand binding affinity. Furthermore, these findings also seem to be able to help better leverage CAPLA for consequent applications, e.g. drug design, etc.In this work, we developed a new deep learning method based on the cross-attention mechanism, called CAPLA, for improved prediction of protein–ligand binding affinity using sequence information of both protein and ligand alone. In particular, CAPLA leverages the cross-attention mechanism to capture the intermolecular correlation of protein-binding pockets and ligands. Comprehensive experiments on various benchmarking datasets demonstrated the superior prediction capability of CAPLA over other state-of-the-art baseline methods. In addition, we demonstrated that CAPLA is capable of revealing the critical residues on the pocket sequence that make greater contributions to the protein–ligand binding through the cross-attention mechanism.There is still some future work that might further improve our method, summarized as follows. First, CAPLA leverages sequence information of proteins and pockets only as the model inputs. Therefore, it is interesting and promising to introduce the 3D structure information of protein–ligand complexes in the model. Second, the input representation of a ligand consists of its SMILES string alone. In the future, we can incorporate more feature representation of ligands into our model, e.g. ligand structures and atom physicochemical properties. Moreover, it is worthwhile to explore the application potentials of CAPLA in drug discovery.btad049_Supplementary_DataClick here for additional data file.


Molecular docking is one of the most widely used computational approaches in the field of rational drug design, thanks to its favorable balance between the rapidity of execution and the accuracy of provided results. Although very efficient in exploring the conformational degrees of freedom available to the ligand, docking programs can sometimes suffer from inaccurate scoring and ranking of generated poses. To address this issue, several post-docking filters and refinement protocols have been proposed throughout the years, including pharmacophore models and molecular dynamics simulations. In this work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of protein-ligand unbinding kinetics, to the refinement of docking results. TTMD evaluates the conservation of the native binding mode throughout a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints. The protocol was successfully applied to retrieve the native-like binding pose among a set of decoy poses of drug-like ligands generated on four different pharmaceutically relevant biological targets, including casein kinase 1δ, casein kinase 2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease. Until the 1980s, the discovery of new drugs was mainly related to empirical observations about the symptoms-relief properties of natural products or serendipity . NowadayAlongside advancements in the field of biophysical approaches for the experimental determination of protein-ligand complex structures, a pivotal role in the last two decades has been played by computational methodologies . These “Among these methods, one of the most widely adopted is certainly molecular docking, a program that is used by medicinal chemists to predict the most plausible ligand accommodation within the active site of a target biomolecule . MoleculAlthough search algorithms are usually very proficient in exploring the conformational space available to the ligand, docking can sometimes produce inaccurate results, due to the limitations of scoring functions . For insTo mitigate these known issues of molecular docking, several refinement protocols have been proposed throughout the years . A firstAnother approach is to rescore docking poses with more computationally demanding methods such as the molecular mechanics/generalized Born surface area (MM/GBSA) method for free binding energy determination . This meFinally, another commonly adopted strategy is to use short-scale molecular dynamics simulations to evaluate the time-dependent pose stability . This apIn the current work, we present the first application of Thermal Titration Molecular Dynamics (TTMD) as an alternative MD-based post-docking refinement tool. Recently developed as an enhanced sampling MD protocol for the qualitative estimation of protein-ligand unbinding kinetics , TTMD evpro. Test cases were chosen coherently with the original work, picking up two “tight binders”  and one “weak binder” (IC50/Kd above the low micromolar threshold). For each biomolecular target, three different protein-ligand complexes available on the Protein Data Bank were chosen. A list of each system investigated in this work can be found in To extend the applicability domain of the TTMD protocol as a post-docking refinement tool, 12 different test cases on four different pharmaceutically relevant targets of interest for our laboratory were investigated: CK1δ, CK2, PDK2, and MAt first, for each protein-ligand system, a self-docking experiment with the PLANTS docking software was executed. The first five docking poses according to the ChemPLP scoring function were used as a starting point for the TTMD simulations. For each investigated docking pose, five independent TTMD replicates were performed. The results for each test case are reported separately and discussed aggregately afterward in the Discussion section.The first test case regarded protein kinase 1 δ, a serine-threonine kinase belonging to the family of CK1 kinases  that is As can be seen in A similar trend compared to the first case can also be observed for ligand D5Q (PDB ID: 5MQV). As reported in Differently from the first two cases, involving “tight binding” ligands, the results of the TTMD refinement were inconclusive for ligand AUG (PDB ID: 5IH6). As can be deducted from The second test case regarded protein kinase 2, a serine-threonine kinase that represents one of the first identified protein kinases . SimilarAs highlighted by As illustrated in In agreement with the results of the TTMD refinement of the CK1δ weak binder, results are inconclusive also for CK2 homologous, ligand V55 (PDB ID: 6HOU). As observable in The third test case involved pyruvate dehydrogenase kinase 2, a member of the GHKL ATPase/kinase superfamily that has a central control role upon cellular energy metabolism ,44 and iAs depicted in Accordingly, like the case of complex 5MQV, where the core of the molecule was placed correctly in most poses and the difference was mainly related to the tail of the molecule which extended out of the binding site, in this case the MS coefficient was also able to distinguish poses with a high RMSD but native-like interaction pattern from poses with high RMSD but incorrect interaction features.Similar behavior can be observed also for ligand 7FW (PDB ID: 5M4M), as noticeable in Contrary to the first two complexes involving strong binders and coherently with what was observed for complexes 5IH6 and 6HOU, the TTMD refinement was also inconclusive for poses generated for ligand W6P (PDB ID: 7EA0). Once again, the poses were all unstable under the chosen simulation conditions , an enzyme playing a pivotal role in this virus replication cycle [pro protein-ligand complex is summarized in The fourth and final test case involved the SARS-CoV-2 main protease , the MS value determined through the TTMD post-docking refinement indicates pose 1, which is superimposable to the crystal reference, as the most stable pose . As depicted in 50 value.Finally, as observed for all previous biological targets, in this case also the TTMD refinement of docking poses generated for the weak binder  was inconclusive. The MS values are high and quite similar although expectable due to the similarity among this set of poses and the crystal reference and the low experimentally determined ICIn the present work, we presented the first application of Thermal Titration Molecular Dynamics (TTMD), an alternative enhanced sampling MD-based method for the qualitative estimation of protein-ligand unbinding kinetics, as a post-docking refinement protocol. TTMD, which combines a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints, was used to rank a series of docking poses based on the persistence of the native binding mode. Four different pharmaceutically relevant biological targets, including protein kinase CK1δ, protein kinase CK2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease were investigated, with three different protein-ligand complexes for each target used in this work. Test cases were chosen among the same protein-ligand complexes used in the original paper , as wellResults reported in this manuscript show how TTMD can be readily used as a post-docking refinement tool for mature, drug-like ligands. The results of the TTMD refinement performed on docking poses generated for this class of ligands showed how the MS score was consistently able to identify the native-like binding pose among the other ones. Furthermore, the MS score demonstrated a good sensitivity in discriminating between poses with partially overlapping interaction features, which could be tricky to evaluate based only on docking calculations.Most MD-based post-docking refinement protocols involve performing short simulations (tens of nanoseconds) at a fixed temperature to distinguish between stable and unstable poses. The discrimination is usually based on simple geometric descriptors such as the ligand RMSD or the center-of-mass distance between the ligand and the protein. Although this strategy seems legitimate and could work in some cases, based on our experience and as can be observed by the detailed trajectory analyses provided in the CS) instead of the ligand RMSD allows for determining a clear cutoff value (zero) that can univocally define whether the loss of interactions found in the starting protein-ligand complex happened. This threshold value is used as an early termination criterion, avoiding spending time simulating the ligand floating around the simulation box thus increasing the feasibility of using such an approach in a high-throughput fashion. Indeed, as can be deduced by On the contrary, thanks to its simple but effective enhanced sampling strategy based on increasing the temperature in between each simulation window, TTMD can emphasize stability differences between relatively similar or partially overlapping docking poses in simulation times comparable to those of classical post-docking refinements . For each macromolecule, three different protein-ligand complex structures were considered: the accession codes for each structure are summarized in The three-dimensional coordinates of each protein-ligand complex used in this study were retrieved from the Protein Data Bank (PDB)  and procpro, for which the catalytically competent homodimer form was reconstituted through symmetric crystallographic transformations. Initially, each structure was pre-processed through the “structure preparation” tool, to assign residues with alternative conformations to the highest occupancy one, to rebuild broken loops through homology modeling, and to fix inconsistencies between the aminoacidic sequence and the experimentally solved structure. Afterward, missing hydrogen atoms were added to the system according to the most probable protonation and tautomeric state at pH = 7.4 through the “Protonate3D” tool. Lastly, each solvent, co-solvent, and ion atom was removed before storing the prepared complex for subsequent calculations.Protein-ligand systems were simulated as monomers, aside from SARS-CoV-2 MFor each protein-ligand complex, a self-docking experiment was conducted. Each ligand was prepared using the “Wash” tool of the MOE 2022.02 suite, assigning the most probable protomeric/tautomeric state at pH = 7.4, rebuilding its three-dimensional coordinates, and attributing the correct partial charges, according to the MMFF94 force field . The binEach protein-ligand complex obtained through docking calculations was prepared for MD simulations through the combination of different packages included in both Visual Molecular Dynamics (VMD ) 1.9.2 aEach MD simulation presented in this work was conducted using the ACEMD  3.5 engi−1 Å−2 was applied for the whole duration of each equilibration run on each restrained atom. For both equilibration stages, the temperature was kept at a constant 300 K value through a Langevin thermostat [Before productive MD simulations, each system was equilibrated in a two-step process. In the first equilibration run, a 0.1 ns simulation in the canonical ensemble was carried out, imposing harmonic positional restraints on both the protein and ligand atoms, leaving the water molecules and the ions unconstrained. In the second equilibration run, a 0.5 ns simulation in the isothermal-isobaric ensemble was performed, with harmonic positional restraints applied only on the protein backbone and on the ligand atoms, leaving the protein sidechain unconstrained. In both cases, a force constant of 5 kcal molermostat , while iermostat .github.com/molecularmodelingsection/TTMD (accessed on 25 January 2023).As thoroughly described in the work of Pavan et al. , ThermalThe user must define the “temperature ramp”, i.e., the temperature values that are sampled throughout the simulation and the time spent simulating at each temperature. Different from the original version of the code, in the current one, the length of each simulation window is not fixed, so different temperatures can be sampled for a different amount of time. In the present work, for consistency with the previous paper, the same temperature ramp was used: the starting temperature was set at 300 K, the end temperature was set at 450 K, the temperature increment between each TTMD-step was set at 10 K, while the length of each TTMD-step was 10 ns. The extension of the temperature ramp must be carefully chosen based on prior knowledge of the target, especially concerning the conservation of the native protein fold throughout the simulation .CS, exploits the Scikit-learn Python module to calculate the cosine similarity between two vectors, i.e., the protein-ligand interaction fingerprints of a reference and a query, where the reference is the protein-ligand complex extracted from the last frame of the second equilibration stage, and the query is each protein-ligand complex obtained from each frame of a certain TTMD-step. Specifically, each interaction fingerprint is an integer vector of r × 8 length, where r represents the number of protein residues and 8 represents the possible type of protein-ligand interaction that can be encoded in the vector . The cosine similarity value is then multiplied by −1 to comply with most scoring functions, where negative values indicate high affinity towards the target and low, near-zero, values indicate lower affinity.As anticipated, the progress of the simulation is evaluated by a scoring function based on protein-ligand interaction fingerprints. This scoring protocol, named IFPCS can range from −1, highlighting a total convergence between the reference (the native binding mode) and the query (the binding pose sampled at a certain time during the simulation), to zero, indicating that each interaction feature of the reference has been lost during the simulation.The values of IFPCS calculated across each frame of the step is calculated: if this value is null, implying that the native binding mode has not been sampled for the whole duration of the step, the TTMD simulation is terminated; otherwise, the simulation proceeds with the next TTMD-step.After the conclusion of each TTMD-step, the average IFPTTMD trajectories were analyzed through an in-house Python script. Specifically, the RMSD of the protein backbone and the ligand are calculated at each frame by exploiting the appropriate function provided by the MDAnalysis package. A time-dependent per-residue decomposition of the protein-ligand interaction energy is calculated by exploiting the NAMD Energy plugin 1.4  for VMD.CS value with the temperature of each step: the slope of the straight line linking the initial and the final state of the titration is defined as the MS coefficient and is used as a proxy measure for estimating the “residence time” of the investigated pose.The first plot, defined as the “titration profile”, correlates the average IFPThe MS coefficient ranges from zero (indicative of a tight binding/high persistence of the native binding mode) to 1 . For each investigated protein-ligand complex, five independent TTMD simulations are carried out, with the average MS coefficient being calculated across three different replicates, after discarding the highest and the lowest value.CS score.The second plot reports the time-dependent per-residue decomposition of the protein-ligand interaction energy for the 25 protein residues that are most frequently contacted by the ligand throughout the trajectory (using a distance cutoff of 4.5 Å). The third and final plot reports the time-dependent evolution of the ligand and protein backbone RMSD and the IFP


Mycoplasma pneumoniae is viable independently from host cells or organisms, despite its strongly reduced genome with only about 700 protein-coding genes. The investigation of M. pneumoniae can therefore help to obtain general insights concerning the basic requirements for cellular life. Accordingly, M. pneumoniae has become a model organism for systems biology in the past decade. To support the investigation of the components of this minimal bacterium, we have generated the database MycoWiki. (http://mycowiki.uni-goettingen.de) MycoWiki organizes data under a relational database and provides access to curated and state-of-the-art information on the genes and proteins of M. pneumoniae. Interestingly, M. pneumoniae has undergone an evolution that resulted in the limited similarity of many proteins to proteins of model organisms. To facilitate the analysis of the functions of M. pneumoniae proteins, we have integrated structure predictions from the AlphaFold Protein Structure Database for most proteins, structural information resulting from in vivo cross-linking, and protein-protein interactions based on a global in vivo study. MycoWiki is an important tool for the systems and synthetic biology community that will support the comprehensive understanding of a minimal organism and the functional annotation of so far uncharacterized proteins.The human pathogen  Mycoplasma are characterized by their strongly reduced genomes that still encode all the functions required for autonomous growth. Bacteria such as Mycoplasma genitalium and Mycoplasma pneumoniae have genomes of only 480 and 816 kb and encode about 480 and 700 proteins, respectively. These small genomes have put these bacteria into the spotlight of systems and synthetic biology, two recent disciplines in biology that aim for a complete understanding of all processes in a living cell up to mathematic modeling and for the creation of artificial forms of life, respectively.Bacteria of the genus M. pneumoniae has become one of the model organisms of systems biology. Many aspects of its biology such as metabolism, DNA and protein modifications, the micro-proteome, protein degradation, regulatory networks, and gene essentiality have been studied at the global level as well  is built upon the same framework as the aforementioned databases SubtiWiki and SynWiki .The structure of MycoWiki gives access to the Gene pages via a search bar, which can be used to query genes by unique identifiers . Genes can also be identified via their locus tags, which are largely based on genome re-annotations  are provided.Moreover, the top bar of the front page gives access to an overview of the functional categories each gene/protein was assigned to, a list of essential genes, and to a random gene page. Finally, it allows the user to log into the database. These links also appear on all gene pages in the right-side bar . The Interaction overview displays a graph of the protein-protein interactions between the protein and its interaction partners. Proteins are represented by nodes that can be clicked to open the corresponding gene pages. In addition, the edges, which depict interactions, link to relevant publications.In  top bar  contains top bar , the genly BLAST  the nuclly BLAST , which a sidebar , a groupFurther down below on the page , additioMycoWiki and its siblings SubtiWiki and SynWiki feature various browsers, which are interactive, graphical displays that allow users to explore certain types of data in an intuitive manner. When they are accessed via a gene page’s top bar, the data corresponding to that gene are highlighted. However, data on other genes can be easily loaded in the browsers by the use of search bars, which allows the user to compare and contrast information on multiple genes effortlessly. These search bars are located in the top left corner of any browser. MycoWiki currently features four different browsers.Genome Browser    and strunk (PDB) . In addiractions .Structure Viewer can be found on the gene page sidebar. While it features full functionality, a full-screen view is also available (shown in the figure), which includes information on how to control the viewer. By using the arrow icons, the user can cycle through the available structures of a protein, which are also found in the main body of the gene page in the section “The protein > Structure.” An info text in the bottom left corner indicates the currently viewed structure, and also links to the respective PDB or AlphaFold DB page. The user can choose different molecular representation styles from the drop-down selection in the upper left corner, such as renderings of the protein surface indicating hydrophobicity or electrostatic values. The visualization of structures and cross-links is performed with NGL Viewer, a web-based tool for molecular 3D graphics  cross-links were extracted and mapped to the AlphaFold structure predictions of the corresponding proteins. Of the 686 predictions assigned to MycoWiki genes, internal cross-links were available for 441 structures. Cross-linked residues of a protein are highlighted in the viewer by dashed lines indicating the Euclidean distance between them. Furthermore, if the distance between the cross-linked residues is smaller than the spacer arms of the respective cross-linker, a molecular 3D representation of the linker is fitted to the structure. This representation was calculated and rendered using the program Xwalk, which determines the Solvent Accessible Surface Distance (SASD) between cross-linked amino acids (Structure Viewer, the visibility of the different distance representations can be toggled via controls at the side of the viewer panel. In addition, a download link for an archive file of all structures and cross-link data is provided.As shown in the figure, visualization of cross-linking data is also available in vo study , in whicno acids . It corrMycoWiki platform shares its framework with its predecessor SubtiWiki (SubtiWiki and SynWiki exist in presentation due to differing availability of data for the corresponding organisms, and some frontend features slightly vary in design.The ubtiWiki . AccordiMycoWiki contains a mixture of manually curated information, which is gathered from recent publications and evaluated by experts, and individual bulk data imports from existing data sources, such as other databases or published experimental data. The platform received a lot of its original annotation from the database MyMpn (MycoWiki, many parts of MyMpn could be directly adopted, such as genomic coordinates, enzyme commission numbers, and post-translational modifications. The main body of information on protein-protein interactions comes from a global in vivo study (se MyMpn , which wvo study .SubtiWiki and SynWiki, a specialized set of categories was conceived for MycoWiki. They are represented by a tree-like structure that classifies genes by their functions, but also groups them according to their localization, essentiality, or quality of characterization, among others. SynWiki, however, “Virulence and pathogenicity” has been added as a top-level category to help characterization of the organism as a pathogen.Similar to SubtiWiki and SynWiki, a list of precomputed best-hit protein homologs in selected related bacteria based on a FASTA pipeline (Mycoplasma genitalium, Mycoplasma mycoides subsp. mycoides, the artificial synthetic organism M. mycoides JCVI Syn3A, Escherichia coli, and Bacillus subtilis. Identity and similarity scores are given for each potential homolog, and an indication as to whether the homolog in question is also the best hit for the protein in the other direction.As in pipeline  has beenpipeline  can be aMycoWiki is open to contributions from all scientists in the field of Mycoplasma research. This is a major distinctive feature as compared to other databases that include information on M. pneumoniae. In addition, MyMpn, as mentioned above, has not received updates in the past years. BioCyc, a large suite of databases for many species including M. pneumoniae (MycoWiki is freely accessible to the scientific community. Finally, PATRIC, the Pathosystems Resource Integration Center (MycoWiki will become a valuable tool for the Mycoplasma research community.To keep functional genome annotation up to date, joint efforts of the corresponding scientific community are required. Therefore, eumoniae , is onlyn Center , has a sMycoWiki, the Mycoplasma research community is invited to register and participate in the curation of the database. While access to the complete contents is free for everyone, only registered users are able to log in and contribute. The entries will be curated by the team behind MycoWiki to ensure continuous high quality of the information provided.To further enhance the information provided in MycoWiki, we have released a new comprehensive model organism database for the minimal bacterium M. pneumoniae. It utilizes the popular framework of SubtiWiki to facilitate intuitive exploration of the available annotation. Particular focus is put on the interactions between different genes and proteins, which may support the scientific community in the development of novel research hypotheses. Customized categories are used to classify the functions and other qualities of genes and their products concisely. In addition, the inclusion of a homology analysis could help to infer the functional annotation of genes. AlphaFold structure predictions have been assigned for the proteins, allowing a visual representation even in cases where no experimentally determined structure exists. While the rendering of internal cross-links for these structures can give an idea about the quality of the prediction, interprotein cross-links could be integrated in the future to explore the interaction between proteins in more detail. We hope that MycoWiki will become a valuable tool for the M. pneumoniae research community, and in turn an asset to the ongoing systems and synthetic biology research. Moreover, the wealth of information provided in MycoWiki and the easy access to classes of proteins based on the categories will help in the further development of M. pneumoniae as a chassis to target therapeutical compounds.With MycoWiki, SubtiWiki, and SynWiki, we will develop novel features including protein-RNA, RNA-RNA, and protein-metabolite interactions that will certainly enhance the value of the databases.For the family of databases that includes The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.CE, BZ, and TP developed the framework of the database and integrated the data. BH performed initial work for the development of the structure viewer and integration of cross-link data. ML-S and LS provided data for the database. JS provided funding and supervised the development of the database. CE and JS wrote the manuscript. All authors read and approved the current submission.


GRIN2B) have consistently been linked to West syndrome, intellectual impairment with focal epilepsy, developmental delay, macrocephaly, corticogenesis, brain plasticity, as well as infantile spasms and Lennox–Gastaut syndrome. It is unknown, however, how GRIN2B genetic variation impacts protein function. We determined the cumulative pathogenic impact of GRIN2B variations on healthy participants using a computational approach. We looked at all of the known mutations and calculated the impact of single nucleotide polymorphisms on GRIN2B, which encodes the GluN2B protein. The pathogenic effect, functional impact, conservation analysis, post-translation alterations, their driving residues, and dynamic behaviors of deleterious nsSNPs on protein models were then examined. Four polymorphisms were identified as phylogenetically conserved PTM drivers and were related to structural and functional impact: rs869312669 (p.Thr685Pro), rs387906636 (p.Arg682Cys), rs672601377 (p.Asn615Ile), and rs1131691702 (p.Ser526Pro). The combined impact of protein function is accounted for by the calculated stability, compactness, and total globularity score. GluN2B hydrogen occupancy was positively associated with protein stability, and solvent-accessible surface area was positively related to globularity. Furthermore, there was a link between GluN2B protein folding, movement, and function, indicating that both putative high and low local movements were linked to protein function. Multiple GRIN2B genetic variations are linked to gene expression, phylogenetic conservation, PTMs, and protein instability behavior in neurodevelopmental diseases. These findings suggest the relevance of GRIN2B genetic variations in neurodevelopmental problems.The GluN2B subunit of N-methyl-D-aspartate receptors plays an important role in the physiology of different neurodevelopmental diseases. Genetic variations in the GluN2B coding gene ( GRIN genes  encoding subunits for N-methyl-D-aspartate-type glutamate receptors, respectively. One among these is GRIN2B, which provides instructions for making a protein called GluN2B [GRIN2B variations have been identified in GRIN2B-related neurodevelopmental disorders, such as ID [GRIN2B-related neurodevelopmental disorders.N-methyl-D-aspartate receptors (NMDARs or GluNRs), which are excitatory glutamate receptors expressed widely in the brain, perform crucial roles in neuronal processes such as neurodevelopment, synaptogenesis, plasticity, learning, and memory . This lad GluN2B . GRIN2B ch as ID , ASD [6,ch as ID , Atypicach as ID , encephach as ID , developch as ID , schizopch as ID , obsessich as ID , and cerch as ID , causingGRIN2B variants have been reported in N-terminal domain (NTD), membrane-proximal ligand binding domain (LBD), transmembrane domain that forms the ion pore containing three transmembrane (TM) spanning domains followed by an intracellular C-terminal PDZ-binding domain (CTD) of variable length [The three-dimensional structure of GluN2B includes conserved domains of the NR2B subunit, i.e., signal peptide (SP) from the residue (1–28), amino-terminal domain (ATD) 28–393) that is involved in receptor assembly, S1 (458–547), S2 (681–798) that forms the ligand-binding domain, re-entrant pore-forming and transmembrane spanning domains (Pore) (556–681) and the C-terminus PDZ domain-binding motif (PDZ) 840–1484), respectively. Disease associated 84, respe93 that ie length ,17,18,19e length . Underste length . A charae length . More soe length ,24,25.GRIN2B gene, as well as their associations with disease and the influence of deleterious nsSNPs on protein structural behavior. Different bioinformatics tools such as SIFT [GRIN2B gene. In addition, ConSurf [The current study sought to identify and forecast pathogenic nsSNPs in the  as SIFT , Polyphe as SIFT , SNAP2,  as SIFT , PhD-SNP as SIFT , I-Mutan as SIFT , and Mu- as SIFT  were use ConSurf  was used ConSurf . AlphaFo ConSurf  was used ConSurf . To expl ConSurf . Through ConSurf  was used ConSurf .http://www.ncbi.nlm.nih.gov, accessed on 1 April 2022) was used to get the nucleotide sequence in FASTA format [https://www.uniprot.org/, accessed on 01 April 2022) [GRIN2B gene were found in the NCBI database of SNPs  [https://www.ensembl.org, accessed on 1 April 2022) [http://www.omim.org, accessed on 1 April 2022) was used to find information on the Glutamate receptor ionotropic gene and protein [NCBI  . SNPs ofil 2022) , and Ensil 2022) . OMIM da protein .https://www.rostlab.org/servces/snap, accessed on 5 April 2022) [SIFT , Polypheil 2022)  online thttp://snps.biofold.org/phd-snp/phd-snp, accessed on 6 April 2022) [http://snps-andgo.biocomp.unibo.it/snps-andgo/snps-andgo/snps-andgo, accessed on 6 April 2022) [PhD-SNP (il 2022)  and SNPSil 2022)  were useil 2022) , REVEL [il 2022) , MetaLR,il 2022)  were emphttp://folding.biofold.org/i-mutant/i-mutant3.0, accessed on 10 April 2022) [SNPs affect protein strength, which may either reduce or improve protein stability. Several strategies were used to boost the confidence in the changes produced to foresee these impacts. I-Mutant3 (il 2022)  is softwil 2022) . The inphttp://ConSurf.tau.ac.il/, accessed on 10 April 2022), was used to analyze GRIN2B protein conservation. ConSurf is a useful software for predicting protein target area high-throughput functions [ConSurf, an online software  [http://www.expasy.ch/sprot/findmod.html, accessed on 20 April 2022).A variety of amino acid alterations occur at post-translational modification sites, resulting in the creation of a varied spectrum of proteins. PTM sites have been found, including methylation, phosphorylation, acetylation, and ubiquitination. These sites play important roles in cellular architecture, such as protein-protein interactions and disease-related signaling cascades. As a consequence, projecting PTM data assists in assessing the influence of polymorphisms on disease association or pathogenicity. The total PTMs were predicted using MuSiteDeep (il 2022) . MuSiteDhttp://servicesn.mbi.ucla.edu/SAVES, accessed on 23 April 2022) was used to validate protein models. It contains six integrated modules, one of which is the RAMACHANDRAN plot, which is used for protein structural assessment and offers information on the conformation of residues in authorized and banned positions.The structural stability of the original and mutant proteins was assessed using structural analysis. Because the full-length GluN2B protein structure has yet to be determined, we predicted the native protein structure using the AlphaFold software , which iThe Desmond program of Schrödinger software 2021-2   with theA 3D matrix representation of the time-dependent migration of amino acid residues. This approach compares C atoms throughout the correlation matrix for all complexes and systems to discover the continuous correlations of domains. The DCCM investigation used 9800 images based on C carbon atoms. The displacements of the backbone C atoms in the trajectories were found to be cross-correlated with the displacements of the C carbon atoms. DCCM has a value between 1 and +1, where a value larger than zero denotes movement with positive correlation (same direction) between two atoms, and a value less than zero represents movement with negative correlation (opposite direction). ProDy was used to evaluate the DCCM , and MatPCA analysis using ProDy was used to analyze the major movements of high amplitude . The diaGRIN2B gene has a total of 1963 single nucleotide polymorphisms that were annotated based on mutation type and loci. There were 838 missense mutations, 623 synonymous mutations, 312 stop gain mutations, and 96 in the protein’s splice region. There were 39 coding sequence SNPs identified, with 28 known to be frameshifts and 12 to be in-frame deletions, 7 splice donors, and 6 splice acceptor variations. The stop codon was maintained in just two variants. The human To get high confidence findings, the SIFT and Polyphen2 servers were utilized concurrently to estimate the impact of SNPs. SIFT and Polyphen2 were fed a total of 25 clinically important nsSNPs. According to SIFT indexing, 24 (96 percent) of the clinically significant nsSNPs were projected to be deleterious with a confidence level of 0.00, while just one SNP was indexed as tolerated. PolyPhen2 which is indexed based on Multiple Sequence Alignment and structural information, classified 20 of the nsSNPs as “deleterious”, 3 as “potentially deleterious”, and just two as “benign”, summing up 25 nsSNPs to be harmful.To get the most confident findings, the servers SNAP2, CADD, REVEL, MetaLR, and Mutation Assessor were utilized to filter out the most deleterious nsSNPs. These 25 nsSNPs were then sent to the SNAP2 server for validation. This study found that 24 of the nsSNPs had an “impact” on protein function, whereas just one was anticipated to be “neutral”. p ≥ 0.80) for PhD-SNP scores was used. Only four nsSNPs  were determined to be neutral by PHD-SNP, whereas the remaining 15 nsSNPs were disease-causing, as predicted by SNPs and GO. The filtered results are shown in The filtered and decreased numbers of polymorphisms were then tested to see whether they were disease-linked or not. The SNP & GO and PhD-SNP servers were then given 19 high-confidence nsSNPs. Each nsSNP was assigned a score to determine if it was neutral or disease-related. To generate high-confidence findings, a cut-off value  enhancing protein stability and the other 16 nsSNPs reducing protein activity by decreasing its stability. MuPro anticipated that none of the nsSNPs would increase protein stability. Any disease-causing mutation is usually discovered in a gene’s highly conserved region. The ConSurf server was used to investigate the conserved behavior of the 25 nsSNPs. On the scale, 9 nsSNPs were highly conserved in addition to being structural and buried, two were conserved and only buried, and five were predicted to be highly conserved in addition to being functional and exposed, while two were just exposed. The one that survived (p.Arg682Cys) was well preserved and exposed. The ConSurf results were represented as color codes ranging from blue to purple, with blue showing variability and purple indicating a highly conserved location. As shown in The MusiteDeep and FindMod servers predicted post-translational modifications linked with our candidate nsSNPs by using the protein sequence as input and an extra peptide mass as input for FindMod. The findings were obtained in .csv format, and all 19 nsSNPs were evaluated. Only three nsSNPs  were shown to contribute to methylation, phosphorylation, and glycosylation, respectively. FindMod also discovered two nsSNPs (p.Arg682Cys and p.Asn526Pro) in peptides that contribute to 2,3-didehydroalanine (Ser) and bromination. p.Asn615Ile, GluN2Bp.Thr685Pro, GluN2Bp.Arg682Cys, and GluN2Bp.Ser526Pro) were constructed with UCSF Chimera using the “aaswap” command-line method. RAMACHANDRAN plot structure validation revealed that 93.32 percent of the anticipated native structure’s residues occupy space in the most preferred area, while 4.77 percent occupy space in the additionally favored region. The verified structures were subjected to molecular dynamics modeling.Our findings show that mutations in the GluN2B protein at p.Thr685Pro, p.Arg682Cys, p.Asn615Ile, and p.Ser526Pro induce pathogenicity. We picked these four highly conserved mutations based on their prediction scores to investigate the protein structural changes they produce. Using AlphaFold, we obtained 10 templates, of which 7EU8.B exhibited 100 percent identity despite only covering 56 percent of the query sequence. This template served as the foundation for our model’s backbone and structure prediction. The comparison models were created using the Discrete Optimized Protein Energy (DOPE) evaluation score to differentiate between “good” and “poor” models. WT for the first third of the simulation, however, the RMSD tends to converge at 12 Å for the latter part. Detailed analysis of the three-dimensional structures revealed that during the first 20 ns, the major contributor to the conformational shift was loops which changed drastically during that period, while α-helices also contributed to the change in conformation. On the other hand, GluN2Bp.Asn615Ile was more stable at 9 Å for the first half of the simulation, undergoing some conformational changes before converging on the same. GluN2Bp.Thr685Pro got stable after 50 ns and remained stable till the end of the simulation. GluN2Bp.Arg682Cys embraced seasonal stability while exhibiting more fluctuation occasionally. GluN2Bp.Ser526Pro remained unstable till the end of the simulation time. The overall trend in the RMSD values was relatively more distant from the GluN2BWT, which shows that upon point mutation, the GluN2B experiences a change in stability  and Thr626 (9.99 Å).The collective flexibility of all the systems was inferred from residual fluctuations of the GluN2Bp.Asn615Ile suggested that Cys588-Pro595 and His848- Ser865 exhibited more than 10 Å with Gly592 posing 11.92 Å and Val853 posing 13.94 Å. The other fluctuating peaks were Asp447 (6.69 Å), Thr626 (8.74 Å), and Asn806 (6.33 Å). The scores of GluN2Bp.Thr685Pro suggested that Phe577-Gly597 and His840-Ile872 exhibited more than 10 Å with Arg593 posing 14.68 Å and Cys854 posing 19.06 Å. The other fluctuating peaks were Leu209 (7.96 Å), Asp447 (7.73 Å), and Gly626 (11.10 Å). The RMSF of GluN2Bp.Arg682Cys suggested that Ser810 and Arg593 exhibited the highest RMSF of 9.69 Å and 9.01 Å. The flexibility of GluN2Bp.Ser526Pro depicted that Gly592, Ile834, and Thr544 exhibited 9.91 Å, 9.7, and 9.2 Å. The flexibility analysis depicted variable residues within the Loop and C-terminal regions contributing more to the overall flexibility with variable intensities , rather than strands (15.17%). On the other hand, the p.Asn615Ile displayed an overall percentage of SSE that was 49.93 percent, with helices making up 34.56 percent and strands making up 15.36 percent. p.Thr685Pro exhibited a total SSE of 49.19% with helix 34.24% and strand 14.95%, while p.Arg682Cys exhibited 48.32% SSE with helices counting for 33.17% and strands contributing 15.16%. p.Ser526Pro has 50.05% of SSE with helices counting for 35.29% and strands accounting for 14.76%. When we further investigated why the GluN2B variant posed less stable and variable conformations, we discovered that the GluN2B residues at the end of the SSE graph (Secondary structure elements (SSE) that contribute to the overall protein stability were also analyzed for both systems, and it was discovered that the GluN2BSE graph  were traWT and four variations, GluN2Bp.Asn615Ile, GluN2Bp.Thr685Pro, GluN2Bp.Arg682Cys, and GluN2Bp.Ser526Pro, overlaid at various timelines. Mutations alter the creation of coiling and the angles between folding, resulting in a change in the overall structure of proteins. To determine the modifications, the mutant structures were overlaid on the original structures using a structural alignment tool, which demonstrated the change in 3D domain development at the location of the mutation  approach to determine the functional displacement of all systems as a function of time. The findings demonstrate that the GluN2B residues at the N and C terminals correlated positively. Mutant GluN2BGluN2BWT . OverallWT and four variations, GluN2Bp.Asn615Ile, GluN2Bp.Thr685Pro, GluN2Bp.Arg682Cys, and GluN2Bp.Ser526Pro. The coordinate covariance matrix computed from the time series of 3D positional coordinates of distinct variant models during the 100 ns MD simulation duration was chosen as the input for principal component analysis (PCA). The findings demonstrate that all of the systems, GluN2BWT and four variations, GluN2Bp.Asn615Ile, GluN2Bp.Thr685Pro, GluN2Bp.Arg682Cys, and GluN2Bp.Ser526Pro, had diverse patterns and did not converge from one energy state to another, indicating that the mutant states of the GluN2B protein had an unstable pattern of conformations  were the most harmful nsSNPs. The disparity in the findings of the prediction software employed in this work highlighted the need to employ more than one method to evaluate the influence of alterations on the structure and function of the protein [The goal of this computational research is to use prediction methods to examine the effects of nonsynonymous SNPs on protein structure and function. The dbSNP database was used to gather nsSNPs. These were put through thirteen prediction algorithms. At least 9 different computer programs found that 19 mutations in the  protein .The conservation analysis findings revealed that all these nsSNPs were found in protected areas. Indeed, the highly conserved amino acids, according to Miller and Kumar, are found in physiologically active places. The biological activities are changed when these residues are replaced . After YIt should be highlighted that the p.Ser526Pro variant was found in the S1 domain, p.Asn615Ile in pore while p.Arg682Cys and p.Thr685Pro were found in the S2 domain; S1 and S2 form the ligand-binding domain, pore; re-entrant pore-forming and transmembrane spanning domains, which consequently may account for the change in spatial conformation and, as a result, instability of the “GluN2B subunit of N-methyl-D-aspartate receptors” complex, which may be a beneficial risk factor for neurodevelopmental disorders. As a result of harmful SNPs, loss or gain of hydrogen bonds, hydrophobic interactions, and salt bridges may change how proteins are built and how they work ,67,68.Bioinformatics techniques now use simulation to investigate the various effects of protein mutations . Moleculp.Asn615Ile, GluN2Bp.Thr685Pro, GluN2Bp.Arg682Cys, and GluN2Bp.Ser526Pro contributed more variable instability to the GluN2B native protein.RMSD analysis showed that the changes to the proteins GluN2Bp.Asn615Ile, GluN2Bp.Thr685Pro, GluN2Bp.Arg682Cys, and GluN2Bp.Ser526Pro. Studies of evolutionary stability and mutations that affect genes that code for proteins have shown that leucine, serine, and arginine are the amino acids that most affect the stability of proteins in mutants [These harmful SNPs may cause the most damage to protein stability. According to Yue and Moult, 25% of pathogenic SNPs in the human population may affect protein function via protein stability changes ,77. Furt mutants .p.Asn615Ile, GluN2Bp.Thr685Pro, and GluN2Bp.Ser526Pro were more tightly packed than the wild-type protein except for GluN2Bp.Arg682Cys. These results were further validated by DCCM, PCA, and conformational analysis.The RMSF calculations in our research revealed that the 11 nsSNPs affect flexibility at distinct levels. Flexibility is an important feature of protein function. It enables proteins to respond to environmental and chemical changes. Molecular flexibility regulates various processes, including enzymatic catalysis and protein activity regulation. Indeed, a change that impacts protein flexibility has the potential to interfere with their function and, as a result, disease development ,81,82. RAccording to the following research, we discovered four pathogenic variants among our findings that were shown to be involved in disease pathogenesis. Lemke et al. conducted studies on two people with West syndrome and significant developmental delay, as well as one person with ID and focal epilepsy. The ID patient had a missense mutation in the extracellular glutamate-binding domain (p.Arg540His), while both West syndrome patients had missense mutations in the NR2B ion channel-forming re-entrant loop . Subsequent testing of 47 individuals with unexplained infantile spasms revealed no new de novo mutations . p.Thr68GRIN2B gene. Because of their presence in a highly conserved area and capacity to impact protein stability, we identified 19 nsSNPs  as potentially harmful that may change the structure and/or function of the GRIN2B encoded N-methyl D-aspartate receptor subtype 2B protein. The detailed analysis of our study’s findings highlighted the significance of variation at the Thr685, Arg682, Asn615, and Ser526 positions involved in post-translational modifications directly or indirectly, which may be at the root of the destabilization of the GluN2B and, as a result, the occurrence of the disease. p.Thr685Pro, p.Arg682Cys, p.Asn615Ile, and p.Ser526Pro were identified as pathogenic, with the potential to inflict significant functional and stability impacts on the proteins. The outcomes of this research are succinct evidence that these findings might serve as a baseline for possible diagnostic and therapeutic approaches.This is the first comprehensive in silico examination of functional SNPs in the