Interactions in the updated version of the human interactome resource (HIR V2) were inferred from 36 mathematical characterizations of six types of data that suggest functional associations between genes. This update of the HIR consists of 88 069 pairs of genes , representing functional associations that are of strengths similar to those between well-studied protein interactions. Among these functional interactions, 57% may represent protein interactions, which are expected to cover 32% of the true human protein interactome. The gene set linkage analysis (GSLA) tool is developed based on the high-quality HIR V2 to identify the potential functional impacts of the observed transcriptomic changes, helping to elucidate their biological significance and complementing the currently widely used enrichment-based gene set interpretation tools. A case study shows that the annotations reported by the HIR V2/GSLA system are more comprehensive and concise compared to those obtained by the widely used gene set annotation tools such as PANTHER and DAVID. The HIR V2 and GSLA are available at  Over the last two decades, advancements in omics technology have provided a set of powerful tools for better elucidation of the mechanisms of human diseases and for the acceleration of drug discoveries . CompareAlthough omics technology offers several opportunities in human research, it also enables resolving many challenges, such as achieving efficient analysis and interpreting vast and complicated omics data . To descActually, the OTCs can be successfully summarized into established biological concepts in many cases through the above strategies. In practical use, however, enrichment-based methods are frequently reported to yield only conceptually general terms  and have even been reported to not enrich any annotation term. Similar to the no annotation term case, the conceptually general terms also provide little assistance to human research because no established biological concepts can be used to accurately describe the OTCs. However, if no established biological concepts exist to accurately describe the OTCs, we sometimes still need the established concepts to interpret the functional impacts of the OTCs. For example, OTCs may lead collectively to GO:2000563 , even when the OTCs themselves are not enriched in these terms (please see the section ‘Discussion’ for details).To meet this challenge, we developed gene set linkage analysis (GSLA) to interpret the potential functional impacts of the OTCs, especially when there are no established biological concepts or suitable concepts available to describe these changes. GSLA can classify an OTC as an established biological function if this OTC has strong functional associations with genes in the established biological process.Arabidopsis transcriptome analyses . The updated version of the HIR includes 88 069 functional gene associations, which are expected to cover 32.48% of the protein–protein interactions in humans. Approximately 57.04% of these functional associations are expected to represent protein–protein interactions. 20 432 of 88 069 functional gene associations are in common between HIR V2 and HIR 2013. A case study also shows that biological processes identified by the HIR V2 and the GSLA web tool were more comprehensive and informative for experimental investigators compared to the widely used annotation tools PANTHER   , 288 375aranoid) . From thIn addition to the above six types of evidence, protein–protein interactions were also considered to be evidence of high-strength functional interactions between genes . In thisThirty-six feature values of six types of functional association evidence were utilized to characterize the functional interactions between human genes (Supplementary Table S1). The detailed equations are on the HIR V2 website. These 36 feature values include 1 homologous interaction feature, 3 phylogenetic profile features, 23 domain interaction features, 4 subcellular colocalization features, 2 coexpression features and 3 shared annotation features (Supplementary Table S3).To successfully separate protein interactions from random gene pairs, not all of these 36 features are suitable. Therefore, only those features showing strong correlations with functional associations were retained, based on which we could decrease the signal-to-noise ratio in the subsequent step of functional association interference. To evaluate the power of the functional association indication of our selected 36 feature values, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was preferred. When computing the protein–protein interaction inference, each feature value with different cut-offs will lead to a series of sensitivities and specificities. We plotted the sensitivities and specificities related to different cut-offs as the ROC curve . Feature values with AUCs higher than 0.6 were considered informative, indicating strong functional associations . Eventually, a total of 18 features with AUCs higher than 0.6 were selected for the subsequent prediction of functional associations between human genes .The LibSVM package was used to train and predict functional associations , 27 Fig. We chosFor the prediction model training, we used the soft-margin Gaussian kernel SVM algorithm. Two parameters, σ (kernel width) and C (soft margin), were used to obtain an optimal harmonic mean of the sensitivity and specificity and were optimized with a 5-fold cross-validation. We trained the prediction model with the optimized σ and C. An external validation dataset with 435 protein interactions (published after 31 December 2017) and randomly generated negative examples were used to validate the prediction model. This model showed a sensitivity of 32.48% and a specificity of 99.98%. Moreover, we evaluated the sensitivity of HPRD, HI-III, HIPPIE, STRING and UniHI to see how well the predicted interactions in each database covered these new interactions. The comparison results are shown in Supplementary Table S4.After we applied this model to all human gene pairs, a total of 83 125 predicted functional associations were obtained. In addition to these inferred functional interactions, we added 4944 experimentally reported interactions to the HIR V2 dataset, which includes 88 069 interactions. 20 432 (23.2%) of 88 069 functional interactions are shared with HIR 2013 . Since 2013, researchers have generated abundant data resources that can be used to generate a high-quality functional interactome of human.The following equation was used to estimate the proportion of protein−protein interactions that were covered by the predicted functional interactome in humans.5, which corresponds to one protein interaction among 1230 gene pairs. This result is similar to the reported fraction of protein interactions in yeast  and the estimated sensitivity  and the evaluation stage sensitivity (32.48%)), the predicted interactions in the HIR V2 are expected to include 86 359 protein interactions. Therefore, 57.04% of the HIR V2 functional interactions (49 249 out of 86 359) are expected to represent protein interactions.where  . Based P < 0.001 for Q2 as the default criteria for GSLA when reporting the functional associations between two gene sets.The GSLA web tool was first developed together with the predicted Human Interactome Resource (HIR 2013)  to interTo deploy the online database, we used the LNMP system, which is an integrated system that includes Linux, Nginx, MySQL and PHP. The MySQL database was used to store data. The web interface of the online database was developed using the Laravel framework using PHP. The front end of the online database was implemented with the Vue.js script library, which implements a single-page application (SPA). Vue.js is an open-source JavaScript library designed for SPA web interface creation. Cytoscape  was usedTo evaluate the quality of the updated version of the functional gene association interactome in humans, we measured the ability of the HIR V2 to group functionally associated genes together. In this study, we assessed the function prediction performance of a gene with its network neighbours. We compared the quality of our predicted functional interactome with other human interactomes in a guilt-by-association gene function prediction assay, including HIPPIE , HPRD 3, PICKLE A precision-recall curve was used for the comparison of the overall prediction performance of new annotations across seven interactomes. Recall measures the proportion of these 118 748 new annotations that are successfully predicted, while precision measures the proportion of PANTHER-predicted annotations that are consistent with the known annotations (both new and old annotations are included). The inclusion of the old annotations in precision measurement may bias the precision estimates because the shared GO annotations were used as a prediction feature to generate the functional gene association network. Each annotation predicted by PANTHER has an enrichment significance value. Setting a higher cut-off value will result in more reported annotations and a higher recall but also a higher false-positive rate. In contrast, setting a lower cut-off value will result in fewer reported annotations and a lower recall but also a higher precision. Therefore, the precision-recall curve has the advantage of showing precision and recall rates on different cut-offs so that a more comprehensive view of the interactome quality can be achieved. A higher AUC of the precision-recall curve indicates a better interactome that supports the ‘guilt-by-association’ prediction of gene functions.As shown in The interface of our developed HIR V2 is user-friendly. The HIR V2 has two search modes: a single gene search mode and a multiple gene search mode . We provOn our HIR V2 website, users can access the GSLA online service to interpret the potential functional impact of an uploaded gene set. et al. due to the limited amount of research on the modulation of the DC function by human Tregs (Regulatory T cells (Tregs) play a pivotal role in maintaining immune homeostasis, including the maintenance of immune tolerance to the self and the prevention of excessive immune responses . The supan Tregs . They diet al. reported that Treg-DCs are a discrete population of mature-DCs and immature-DCs. Compared to mature-DCs, 51 and 93 Treg-DC genes were significantly over- or underexpressed. In this study, we reanalysed the differentially expressed genes in the microarray dataset GSE72893  . Mavin eGSE72893 . As show (et al. . and Fra (et al. .To build the reference interactome for humans, many efforts have been made prior to our study. To date, many human interactomes have emerged that provide experimentally reported protein−protein interactions or predicted molecular interactions. For example, BioGRID  and IntABased on our high-quality HIR V2, GSLA is able to interpret the functional impacts of OTCs in humans. The high precision and high coverage of the HIR V2 can help GSLA report significant functional associations between gene sets. The strategy of GSLA is to evaluate the density of functional gene interactions between individual genes in two gene sets. The previously developed interactomes cannot satisfy this requirement, as we described above. After the evaluation of the functional impact prediction of these interactomes, the HIR V2 showed the best performance. The HIR 2013 also faced this phenomenon . The powThe HIR V2/GSLA system extends the capability of the existing enrichment-based gene set annotation tools. Enrichment-based annotation tools categorize the OTCs into established biological processes. Here, GSLA shows the advantage of interpreting the functional impacts of OTCs when there is no established biological concept. In this case, other enrichment-based tools cannot give instructive annotations, while the HIR V2/GSLA system may still help investigators better understand how the observed change connects to related physiologies. In addition, the HIR V2 provides a useful and high-quality functional association resource to researchers that enables them to describe the molecular mechanism of their genes of interest.The HIR V2 database contains experimentally reported interactions integrated from major interaction repositories and the most comprehensive prediction of the human interactome with a high reliability. The website of HIR V2 features a user-friendly query interface, providing rich annotation on the relationships between two proteins. A graphical interaction network browser has also been integrated into the web interface to facilitate the mining of specific pathways. HIR V2 is not only a resource for large-scale mining of human interaction networks but also an exploratory tool for cell/molecular biologists to understand more about the relationships between the proteins in specific cellular processes.Not applicable.Not applicable.http://human.biomedtzc.cn.The predicted interactome HIR V2 and its associated GSLA web tool are available in Database Online.baab009_SuppClick here for additional data file.


ATL1 gene and to predict their impact on the functional and structural levels by several computational analysis tools.  Hereditary spastic paraplegia type 3A (SPG3A) is a neurodegenerative disease inherited type of Hereditary spastic paraplegia (HSP). It is the second most frequent type of HSP which is characterized by progressive bilateral and mostly symmetric spasticity and weakness of the legs. SPG3A gene mutations and the phenotype-genotype correlations have not yet been recognized. The aim of this work was to categorize the most damaging SNPs in ATL1 gene were retrieved from dbSNP database and then run into numerous computational analysis tools. Additionally; we submitted the common six deleterious outcomes from the previous functional analysis tools to I-mutant 3.0 and MUPro, respectively, to investigate their effect on the structural level. The 3D structure of ATL1 was predicted by RaptorX and modeled using UCSF Chimera to compare the differences between the native and the mutant amino acids.  The raw data of  Five nsSNPs out of 249 were classified as the most deleterious . in silico tools that revealed five nsSNPs  are deleterious SNPs, which have a functional impact on ATL1 protein and, therefore, can be used as genomic biomarkers specifically before 4 years of age; also, it may play a key role in pharmacogenomics by evaluating drug response for this disabling disease. In this study, the impact of nsSNPs in the ATL1 gene was investigated by various  Hereditary spastic paraplegia type 3A (SPG3A) is a neurodegenerative disease inherited type of Hereditary spastic paraplegia (HSP) . SPG3A iATL1 gene; ATL1 is a protein coding gene which is located in chromosome 14 at position 22 in the forward strand (14q22.1). It provides instruction for production of atlastin-1 protein, which is a member of guanosine triphosphatases (GTPases) family. It is primarily produced in the brain and the spinal cord in the central nervous system. In neurons, this protein is located mainly in the endoplasmic reticulum and it is responsible for endoplasmic reticulum tubular network biogenesis; also, it is located at the tip of neurons in the axonal growth cone, it directs the growth and development of the axons which transmit nerve impulses [ATL1 gene are more common with early-onset HSP patients. [ATL1 gene mutations likely disrupt neuron function, and it leads to impairment of the distribution of materials within these cells and restriction in the growth of axons of neurons. These problems can lead to abnormal functioning or death of the neurons in the corticospinal tracts. As a result, they will become unable to transmit nerve impulses, particularly to other neurons and muscles in the lower extremities. This impaired function leads to the signs and symptoms of spastic paraplegia type 3A.Insight into the molecular basis of HSP is increasing rapidly; different genes cause clinically indistinguishable HSP types –13. SPG3impulses . Mutatioatients.  These muatients. . Small iatients. . ATL1 gehttp://exac.broadinstitute.org/) claim a rare benign SNPs also found in ATL1 in healthy persons. Besides, a rare mutation in the ATL1 has been reported in axonal motor neuropathy [The population databases , while the protein reference sequence was obtained from UniProt database (https://www.uniprot.org/).The data of the SIFT was used to observe the effects of each amino acid substitution on protein function. SIFT predicts damaging SNPs on the basis of the degree of conserved amino acid residues in aligned sequences to the closely related sequences, gathered through PSI-BLAST. .PolyPhen-2 stands for polymorphism phenotyping version 2. We used PolyPhen-2 to study potential effects of each amino acid substitution on structural and functional properties of the protein by considering physical and comparative approaches. The input data needs accession number and position of mutations and native and altered amino acids .It predicts whether an amino acid substitution has an effect on the biological function of a protein grounded on the alignment-based score. If the PROVEAN score is ≤−2.5, the protein variant is predicted to have a “deleterious” effect, while if the PROVEAN score is >−2.5, the variant is predicted to have a “neutral” effect .It is a trained functional analysis web-based tool that differentiates between effect and neutral SNPs by taking a variety of features into account. SNAP2 has an accuracy  of 83%. It is considered an important and substantial enhancement over other methods .It is a support vector machine (SVM) based on the method to accurately predict the disease-related mutations from protein sequence. The probability score higher than 0.5 reveals the disease-related effect of mutation on the parent protein function. .It is an online Support Vector Machine- (SVM-) based classifier, which is optimized to predict if a given single-point protein mutation can be classified as disease-related or as a neutral polymorphism. .Change in protein stability disturbs both protein structure and protein function. I-Mutant is a suite of support vector machine. It offers the opportunity to predict the protein stability changes upon single-site mutations. The FASTA format sequence of ATL1 protein was retrieved from UniProt that is used as an input to predict the mutational effect on protein and stability RI value (reliability index) computed. .It is a support vector machine-based tool for the prediction of protein stability changes upon nonsynonymous SNPs. The value of the energy change is predicted, and a confidence score between -1 and 1 for measuring the confidence of the prediction is calculated. A score <0 means the variant decreases the protein stability; conversely, a score >0 means the variant increases the protein stability. .It is a server to search structural data from several databases such as UniProt. The FASTA format sequence of ATL1 protein was retrieved from UniProt that is used as an input to predict the biophysical validation for our SNPs of interest. The main aims for the submissions in Project HOPE are to analyse and confirm the results that we had obtained earlier .The full 3D structure of human ATL1 protein is not available in the Protein Data Bank. Hence, we used RaptorX to generate a 3D structural model for wild-type ATL1. The FASTA format sequence of ATL1 protein was retrieved from UniProt that is used as an input to predict the 3D structure of human ATL1 protein. .UCSF Chimera is a highly extensible program for interactive visualization and analysis of molecular structures and related data, including density maps, supramolecular assemblies, sequence alignments, docking results, and conformational analysis Chimera (version 1.8) .Geometrical validation through the Ramachandran plot provides a single measure encapsulating the major structure-validation information contained in bond angle distortions. We used the BDP model for ALT1 protein we acquired from RaptorX and submitted it to this software, and then we downloaded the resulted predictions . We alsoIt is a web server which suggests evolutionary conservation reviews for proteins of known structure in the PDB. ConSurf detects the similar amino acid sequences and runs multialignment approaches. The conserved amino acid across species detects its position using specific algorithms. .It is a web server creating proposition about gene function, investigating genelists and prioritizing genes for functional assays. The high accuracy of the GeneMANIA prediction algorithm and large database make the GeneMANIA a useful tool for any biologist. .It is a public archive of reported studies of the relationships among human variations and phenotypes, with supporting evidence. We used it to compare our predicted approach with the clinical one .The Ensembl Variant Effect Predictor software provides toolsets for an organized approach to annotate and assist for prioritization of mutations. The input data format was a list of variant identifiers, while the output was filtered by choosing 1000 genome combined population to expend the population coverage .The total number of SNPs in the coding region that were recovered from NCBI was 249 nsSNPs, and these SNPs were submitted into different functional analysis online tools,  Ninety-eThe in vitro approach is consuming a lot of time and fees which may or may not have a positive result from the study; on the other hand, the computational approach is entirely different; it saves time with low cost and gives rapid results to improve our understanding of how variants could interrupt the protein structure and function , 47.in silico analysis was devoted to the coding region of ATL1 gene, which uncovered five disease-causing mutations that may cause SPG3A. The five deleterious SNPs come after extensive computational analysis, and seven online tools  were recovered from the dbSNP as untested and all were found to be deleterious mutations; while these SNPs (R217Q and R495W) were recovered as pathogenic which agrees with our finding .In order to investigate the biophysical properties of these variants, Project HOPE server was used to serve this purpose; RaptorX was used to create a 3D structure model for ATL1 protein, , while UIn : (T120I)In : (R217Q)In : (R495W)Glycine changes to glutamate at position 495; the wild-type residue is a glycine, the most flexible of all residues. This flexibility might be necessary for the protein's function. Mutation of this glycine can abolish this function. The wild-type residue charge was neutral, the mutant residue charge is negative, and this can cause repulsion of ligands or other residues with the same charge. The torsion angles for this residue are unusual. Only glycine is flexible enough to make these torsion angles, and mutation into another residue will force the local backbone into an incorrect conformation and will disturb the local structure.In The structure predicted through algorithm and software needs to be validated through other software like the Ramachandran plot analysis, which measures the accuracy of the structured data . In our We also used Consurf server to check the conservation region of ATL1 protein, and the result shows 4 SNPs  located in highly conserved regions, which can directly affect the protein function .ATL1 has many dynamic functions: endomembrane system organization, endoplasmic reticulum organization, and protein homooligomerization. The genes coexpressed with, sharing similar proteindomain, or contributed to achieve similar function as shown in , which have a functional impact on ATL1 protein and, therefore, can be used as genomic biomarkers specifically before 4 years of age; also, it may play a key role in pharmacogenomics by evaluating drug response for this disabling disease.In this study the impact of nsSNPs in the


Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning.We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity.https://sites.google.com/view/wajidarshad/software.We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem. Kd) through different experimental methods such as Nuclear magnetic resonance spectroscopy, gel-shift and pull-down assays, analytical ultracentrifugation, Surface Plasmon Resonance (SPR), spectroscopic assays, etc . The results obtained through this evaluation will also serve as a baseline in this study.In order to investigate the accuracy of PPA-Pred2, we evaluated its performance on the selected dataset. For this purpose, we accessed PPA-Pred2 [17] through its webserver , to predict absolute protein binding affinity values rather calssifying protein complexes into low and high affinity as in case of LUPI . To devel) and receptor (r) of each complex. We used several explicit features and various kernel representations to model sequence-based attributes of protein complexes. We discuss the sequence-based features used in this study below.In machine learning based prediction models, we require a feature representation of each example for training and testing Fig. . TherefoϕAAC(s) of a given sequence s such that the ϕAAC(s)k contains the number of times amino acid k occurs in s . We


The current computational methods on identifying conserved protein complexes across multiple Protein-Protein Interaction (PPI) networks suffer from the lack of explicit modeling of the desired topological properties within conserved protein complexes as well as their scalability.Saccharomyces cerevisiae , Drosophila melanogaster , Caenorhabditis elegans , and Homo sapiens , we demonstrate that ClusterM outperforms other state-of-the-art algorithms by a significant margin and is able to identify de novo conserved protein complexes across four species that are missed by existing algorithms.To overcome those issues, we propose a scalable algorithm—ClusterM—for identifying conserved protein complexes across multiple PPI networks through the integration of network topology and protein sequence similarity information. ClusterM overcomes the computational barrier that existed in previous methods, where the complexity escalates exponentially when handling an increasing number of PPI networks; and it is able to detect conserved protein complexes with both topological separability and cohesive protein sequence conservation. On two independent compendiums of PPI networks from ClusterM can better capture the desired topological property of a typical conserved protein complex, which is densely connected within the complex while being well-separated from the rest of the networks. Furthermore, our experiments have shown that ClusterM is highly scalable and efficient when analyzing multiple PPI networks. Advanced high-throughput technologies for measuring protein interactions , 2 have Intuitively, conserved protein complexes should have the following properties with respect to their network topology and sequence homology. Within each species, proteins within a protein complex is densely connected to each other while being loosely connected to and well separated from the rest of the PPI networks . Across In recent years, efforts have been made to develop computational techniques for the comparison of PPI networks across different species through global network alignment –12. MostIn addition to the lack of explicit modeling of the desired topological properties within conserved protein complexes, most of the existing network alignment and clustering algorithms , 17, 18 To fill these critical gaps in a conserved protein complex identification, we propose a scalable algorithm—ClusterM—that explicitly characterizes the desired topological separability of protein complexes and also incorporates sequence similarity of proteins across the given PPI networks. ClusterM consists of three major steps. The first step is to find a set of protein spines  across different PPI networks. In the second step, well-separated subnetworks around proteins in each protein spine are identified. The final step is to look for conserved subnetworks within those identified well-separated subnetworks, which have both cohesive protein-protein interaction similarity and sequence similarity.Last but not least, in order to evaluate and compare the performance of ClusterM and other existing algorithms, we have curated a new yeast-human reference conserved protein complex dataset based on yeast and human gold-standard complexes and propose effective evaluation metrics based on the existing measures for assessing protein complex identification algorithms for individual PPI networks. Experimental results based on comparative analysis of yeast and human PPI networks show that ClusterM clearly outperforms the current state-of-the-art algorithms.Sce) and human (Hsa) PPI networks are obtained from Database of Interacting Proteins (DIP) version 20150101 ..\documenHere, proaches , 17, 18 3. Conserved protein complexes with desired topological and homologous properties (Task 2): The next task is to collect ui with Hi. The homologous correspondence between proteins p and q can be approximated by the protein sequence similarity s∈  :5\documblast is the bit score of the sequence similarity obtained by the local sequence alignment tool BLAST  for each parameter pi. For all the competing algorithms, we set N=100 and report the results with the best performing parameters.Among the tested algorithms, ClusterM and NetworkBLAST-M have only a single tuning parameter, while NetworkBLAST and MaWISh have 5 and 7 tuning parameters, respectively. In order to obtain the best performing parameters for fair comparison of the selected algorithms, we have selected the best performing parameter(s) for each algorithm by grid search. Within at most http://www.ece.tamu.edu/~xqian/ClusterM/. ClusterM is computationally efficient for handling multiple genome-scale PPI networks from different species. The memory consumption of ClusterM depends on the size of the subnetworks to be identified and the runtime of ClusterM can be significantly reduced by parallelization. It takes about 40 minutes for ClusterM to handle four PPI networks in IntActPPIs with 50,876 proteins in total on a laptop computer (16 GB memory and Intel i7 2.9 GHz cpu) with 2-core parallelization.The MATLAB code and all used data are available at


Mammals contain over 200 different cell types, yet nearly all have the same genomic DNA sequence. It is a key question in biology how the genetic instructions in DNA are selectively interpreted by cells to specify various transcriptional programs and therefore cellular identity. The structural and functional organization of chromatin governs the transcriptional state of individual genes. To understand how genomic loci adopt different levels of gene expression, it is critical to characterize all local chromatin factors as well as long-range interactions in the 3D nuclear compartment. Much of our current knowledge regarding protein interactions in a chromatin context is based on affinity purification of chromatin components coupled to mass spectrometry (AP-MS). AP-MS has been invaluable to map strong protein-protein interactions in the nucleus. However, the interaction is detected after cell lysis and biochemical enrichment, allowing for loss or gain of false positive or negative interaction partners. Recently, proximity-dependent labeling methods have emerged as powerful tools for studying chromatin in its native context. These methods take advantage of engineered enzymes that are fused to a chromatin factor of interest and can directly label all factors in proximity. Subsequent pull-down assays followed by mass spectrometry or sequencing approaches provide a comprehensive snapshot of the proximal chromatin interactome. By combining this method with dCas9, this approach can also be extended to study chromatin at specific genomic loci. Here, we review and compare current proximity-labeling approaches available for studying chromatin, with a particular focus on new emerging technologies that can provide important insights into the transcriptional and chromatin interaction networks essential for cellular identity. A long-standing question in cell biology is how the same genome can lead to different cell types. A major driving force that determines cellular identity is their underlying gene expression landscape. Whether a gene is turned on or off depends mostly on its physical accessibility, governed by the local chromatin context . The basThe most widely applied methods to study protein-protein interactions in a chromatin context are affinity purification or immunoprecipitation followed by mass spectrometry (AP-MS/IP-MS). After cell lysis, soluble proteins are captured and enriched by a ligand (bait) coupled to a solid support. The most commonly used ligands are antibodies targeting epitope-tagged (AP) or endogenous (IP) proteins (prey). These ligands are attached to a solid support, in most cases agarose, sepharose or magnetic beads. After enrichment, proteins are analyzed by mass spectrometry to identify proteins interacting with the prey. Consequently, successful application of IP-MS depends on the availability of an antibody or known interactor of the protein of interest. For AP-MS, common antibodies can be used, because an epitope tag is fused to the protein of interest. While these methods can efficiently identify strong protein-protein interactions that are not disrupted after cell lysis and solubilization, transient interactors with lower affinity can be lost during the purification steps – typically performed under high-salt and detergent conditions. In addition, interactions are only detected after lysis and enrichment and thus no longer in its native environment of living cells.Proximity labeling followed by mass spectrometry analysis can address these key limitations of AP- and IP-MS. The basic principle of all proximity labeling methods is to introduce a covalent biotin tag to proteins in the neighborhood of a selected target in living cells. To this end, enzymes convert a supplemented substrate into a highly reactive biotinylated intermediate that then transfers biotin to amino acid side chains in proximity. Spatial restriction of labeling is achieved by fusing the enzyme to the target protein as well as reducing the labeling time. Currently, three major enzymes are used for proximity labeling: biotin ligase , horse radish peroxidase (HRP), and engineered ascorbate peroxidase . After the labeling reaction, cells are lysed and the biotinylated proteins are extracted with streptavidin beads and subjected to mass spectrometry. Identified candidates in proximity with the bait protein can be summarized as the “proxisome” .in vivo covalent biotinylation enables the detection of transient interactions and low abundance proteins. Finally, biotinylation is an infrequent protein modification in many organisms, thus no additional endogenous proteins are part of the background in mass spectrometry analysis   subunit of acetyl-CoA carboxylase. To take advantage of this highly specific reaction, an unnatural substrate mimicking a short peptide sequence was created (a priori.The (bioAMP) . One of  created . This bi created . The new created . In a di created . However∗ (R118G) from E. coli made an unbiased approach possible by disrupting binding of bioAMP to BirA  was developed and achieved efficient labeling for subsequent LC-MS/MS analysis in 30 min  has been used to enhance contrast for electron microscopy by polymerizing 3,3′-diaminobenzidine after OsO4 treatment   . In thispathways . Also, tIn contrast, the APEX2 approach has been developed mainly in mammalian cells and is not easily transferable to other organisms. The main concern is the delivery of the substrate biotin phenol into the cell or nucleus . For yeaRecently, a method named ChromID to study the proxisome of specific histone PTMs was published . In this∗ to the MYC oncoprotein in cultured HEK293 and tumor xenografts confirmed known and identified over 70 new potential interaction partners, ranging from chromatin remodelers to transcription factors . This modification is associated with gene activation and overexpression of NSD2 has been linked to some forms of cancer . To uncout cells . The aut∗, GFP-BirA∗ or NLS-BirA∗ as controls could identify 25 proximal proteins, of which 4 were known and 21 unknown  was used in this study, increasing potential false-negatives. However, they could still generate high confidence hits due to very stringent cut-offs and to the improved statistical power of 12 data sets merged from the two purification approaches.The MCM2-7 complex is known for its helicase activity during replication in S-phase, but has also been associated with DNA repair, chromatin organization and cell cycle regulation . In an ain cells . Followi∗ and immunoprecipitation with P2-HNF4α-GFP in HEK293T or HCT116  revealed an association of P2-HNF4α with DNA repair factors including PARP1, RAD50, and PRKDC. They confirmed these interactions by co-immunoprecipitation with endogenous HNF4α in colorectal cancer cell lines. Here, BioID generated about four times more candidates than AP-MS, but also had a higher background. Interestingly, the DNA repair factors were found in the relatively small overlap of both approaches. This suggests that using both methods simultaneously can potentially provide biologically relevant candidate hits.In a similar approach, the same group probed the interactome of the master regulator HNF4α, which plays a crucial role in development and tumorigenesis . IncreasAnother example of coupling proximity and affinity purification with mass spectrometry is a study investigating the interactome of the DNA repair factor Ku70 . BesidesTogether, these studies indicate that proximity labeling is able to discover physical interactors that are also found in AP-MS experiments. However, the overlap during side-by-side experiments is usually small. Interestingly, ∼50% of candidates of the SOX2 BioID proxisome could be found in at least one of eight different SOX2 AP-MS interactomes . The indAll methods described until now give insight into chromatin interactions that can occur genome-wide without any spatial information. However, it would also be interesting to investigate protein-protein interactions at specific DNA loci, especially in the context of oncogenes. The most commonly used method is a special form of IP-MS called chromatin immunoprecipitation (ChIP). There are two types of ChIP: cross-linked (XChIP) and native ChIP (NChIP). In XChIP, the chromatin is reversibly cross-linked with associating proteins and subsequently sheared by sonication. For NChip, the native chromatin is extensively digested by micrococcal nuclease (MNase). To immunoprecipitate the local chromatin environment, antibodies targeting histone posttranslational modifications or chromatin factors of interest are used. The isolated and purified DNA is then sequenced to allocate genomic locations of the protein-protein or protein-DNA interaction. NChip is mostly limited to histone proteins due to their high abundance and stable interaction with DNA, whereas other proteins are lost without crosslinking during the stringent IP conditions. However, XChIP can generate false positives by crosslinking randomly associating proteins or after cell lysis by non-specific binding of factors to the sheared chromatin or bead material. Furthermore, crosslinking agents distort the native environment of chromatin before analysis .in situ approach, protein A-fused MNase is directed to a specific antibody against the chromatin target of interest and leads to the release of protein-DNA complexes into solution without the requirement of crosslinking agents (Another method is the Proteomics of isolated chromatin segments (PICh), which deploys complementary DNA probes after chemical crosslinking to capture the local chromatin composition . A diffeg agents . As this∗ fusion protein has laid the foundation for an in vivo approach using proximity labeling , first the telomeres and centromeres were targeted, which allowed specific profiling of their subnuclear proteomes . To identify proximal factors of ALT cell telomeres, BioID proximity labeling with TRF1-BirAwas used . By compwas used  to give hTERT and c-MYC promoters  and FKBP-rapamycin binding (FRB) combined with BioID was developed  were developed. In a PCA, two POI are fused to either half of a split reporter protein (enzyme or fluorescent protein). The reporter protein is reconstituted only upon interaction of the POI. However, the exact interaction dynamics between the two split fragments remain unknown, e.g., if the reconstitution is reversible. For proximity labeling, split-BioID and split-APEX2 have now been reported . As the A recent paper indicated that biotin ligase-based proximity labeling may potentially allow the study of intrinsically unstructured regions. These flexible lysine-rich protein domains are more accessible and show faster biotinylation kinetics than structured, less exposed regions . ConsequHU searched the literature, created the figures, and wrote and edited the manuscript. SH provided the guidance and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Prochlorococcus marinus MIT 9303 is a marine cyanobacterium found in sea waters. It was first isolated from a depth of 100 m in the Sargasso Sea in the year 1992. This cyanobacterium serves as a good model system for scientific research due to the presence of many desirable characteristics like smaller in size, ability to perform photosynthesis and the ease of culture maintenance. The genome of this cyanobacterium encodes for about 3022 proteins. Out of these 3022 proteins, few proteins were annotated as hypothetical proteins. We performed a computational study to characterize one of the hypothetical proteins “P9303_05031” to deduce its functional role in the cell using various bioinformatics techniques. After in-depth analysis, this hypothetical protein showed the conserved domain as of Hsp10 of molecular chaperonins of GroES. In this work, we have predicted the bidirectional best hits for the hypothetical protein P9303_05031 followed by the prediction of protein properties such as primary, secondary and tertiary structures. The existence of the Hsp10 domain indicates its role is essential for the folding of proteins during heat shock. This work represents the first structural and physicochemical study of the hypothetical protein P9303_05031 in Prochlorococcus marinus MIT 9303. Upon in4P9303_05031 in the marine cyanobacterium Prochlorococcus marinus MIT 9303 may encode for GroES kind of protein which is responsible for heat shock response.The analysis of the hypothetical protein showed sequence similarity mostly to the chaperonin 10kd subunit which belongs to Heat shock proteins family. By comparing the annotations and the sequences of bidirectional hits obtained from BLASTP searches indicates that the protein has the similar function as that of other cyanobacterial GroES proteins. The domain identified from Pfam and ProDom searches in the protein was characteristics of the cnp10 family domain found in a various diverse group of protein which act as Heat shock proteins. The dominance of coiled regions indicates the high level of conservation and stability of the protein structure. Moreover, the protein-protein interactions also show that the protein is to interact with the hub of Hsps which are responsible for adaption of the survival mechanism of bacteria during heat stress. All these above results lead to a conclusion that the hypothetical protein encoded by the gene Click here for additional data file.


Driven by its successes across domains such as computer vision and natural language processing, deep learning has recently entered the field of biology by aiding in cellular image classification, finding genomic connections, and advancing drug discovery. In drug discovery and protein engineering, a major goal is to design a molecule that will perform a useful function as a therapeutic drug. Typically, the focus has been on small molecules, but new approaches have been developed to apply these same principles of deep learning to biologics, such as antibodies. Here we give a brief background of deep learning as it applies to antibody drug development, and an in-depth explanation of several deep learning algorithms that have been proposed to solve aspects of both protein design in general, and antibody design in particular. In this paper, we outline the deep learning techniques that are starting to be applied to the field of antibody design and their results. We outline current challenges in three areas of antibody design: (1) modeling of structure from sequence, (2) prediction of protein interactions, and (3) identification of likely binding sites. We then touch on the deep learning techniques and algorithms that have been developed towards antibody design in each of these areas. As these three challenge areas have analogues in the general protein space, we additionally describe deep learning approaches for similar problems among proteins more broadly, with the understanding that these approaches may be applicable to the narrower domain of antibody engineering. We also describe the dataset and benchmarks available that could aid in the development and comparison of methods within this field. We conclude with a comparison of these methods and touch on future directions.Monoclonal antibody therapeutics have become an increasingly popular approach for drug development against targets and indications where small-molecule-based approaches have proven insufficient. With this increase in focus has come the creation of a number of new methods for improving and refining the antibody development pipeline. Innovations in constructing display libraries (phage and yeast) have accelerated the candidate discovery timeline and reduced the challenges associated with downstream development of therapeutic leads. A significant goal of current lead development research has been to reduce the necessity of downstream lead optimization steps such as improvements to the solubility and immunogenicity of the candidates, along with the mitigation of other developability concerns. Other research has expanded the mechanisms of action of potential antibody therapeutics by adding additional functional domains to create bispecific and Fc effector antibodies, and by exploring different constructs for their unique properties, such as single-chain variable fragments and camelid-derived nanobodies.in silico engineering and design of antibody candidates. These approaches attempt to harness advances in computational processing power to reduce the cost and increase the speed of lead candidate generation. The advantages of an in silico pipeline would include rapid and cheap scaling of candidate generation, the ability to develop antibodies against challenging antigens, and the application of rational design principles. In contrast to traditional methods of candidate generation such as hybridoma or phage display, an in silico pipeline promises cheaper and faster drug development. However, conventional in silico methods have yet to fully deliver on these promises. Here, we present deep-learning-based approaches that appear to demonstrate greater success than conventional methods with respect to the key challenges of computational antibody design.While these in vitro innovations have shortened timelines and have improved different steps throughout of the development pipeline, there have been a new class of innovations surrounding the Antibodies are a type of protein produced as an immune response to invading pathogens. They consist of four chains—two heavy chains and two light chains. The heavy chains include three constant domains and a variable domain, while the light chains have just one constant domain and one variable domain. The variable domains contain the antibody’s binding surface, or “paratope”. The paratope primarily consists of six distinct variable loops—three on the light chain , and three on the heavy chain  . This reThe specificity and broad applicability of antibodies make them the subject of much attention in medical research, and this has in turn attracted much attention to the study of antibodies computationally, or in silico.In order to computationally analyze an antibody or predict its effectiveness, it is often necessary to generate a three-dimensional model. As traditional structure-determination methods, such as X-ray crystallography, Nuclear Magnetic Resonance (NMR), and Cryogenic Electron Mycroscopy (CryoEM), are laborious, time-consuming, and expensive, computational methods have emerged to generate structure predictions using chemistry and existing protein fold data. Several groups have been able to accurately predict antibody structures for a set of benchmarks, but the modeling of the H3 CDR loop continues to present a significant challenge . The bioAnother challenge in computational antibody development is interface prediction. Typically, the interface between two proteins consists of several well-conserved residues that form a tight interaction. The reason for this is that typically any two interacting proteins will have co-evolved over the span of many generations. An antibody’s interaction with its antigen does not benefit from such a long shared history. Antibodies are ad-hoc binders generated “on the fly” to address a foreign pathogen. Although antibody-antigen interactions do fall under the general umbrella of protein–protein interactions (PPI), it has become increasingly apparent that antibody–antigen interactions and their interfaces are distinct, with unique properties that reduce the applicability of general protein interaction prediction to the antibody space.  As only Deep learning is a subset of machine learning, concerned with algorithms that are particularly capable of extracting high level features from raw, low level representations of data. An example of this is an algorithm which extracts object curvature or depth from raw pixels within an image. Deep learning algorithms generally consist of artificial neural networks (ANN) with one or more intermediate layers. The intermediate layers of an ANN make the network “deep” and can be considered responsible for transforming the low-level data into a more abstract high-level representation. Each layer of the network consists of an arrangement of nodes, or “neurons”, which each take as input a set of weighted values and transform them into a single output value . The resulting values are then passed on to nodes in subsequent layers. The input values for the first layer are chosen by the practitioner or model architect. In a biochemical context, these features can be hand-crafted values such as a protein’s volume, or lower-level values such as an amino acid sequence. In a “deep” network, the outputs of the first layer are passed through one or more intermediate layers before the final output layer which produces the final result. The intermediate layers allow the network to learn non-linear relationships between inputs and outputs by extracting successively higher level features and passing them along to subsequent layers .In order for a neural network to transform an input into a desired output, it must be “trained”. Training a neural network happens by modification of the weights of the connections between nodes. In a “fully connected” network each node is connected to each node in subsequent layers. The output values from the nodes of the preceding layer are passed along weighted connections to the nodes of the subsequent layer. These weights of the connections are typically initially randomized, and as the network is trained, corrections are made by iteratively modifying the weights in such a way that the network is more inclined to produce the desired output from the given input. The correctness of a model is determined by a “cost function”, which provides a numerical measure of the amount of error in a model’s output. The choice of the cost function largely depends on the task of the network and functions as a proxy for minimizing or maximizing some metric which cannot be directly used for optimization since it is non-differentiable, such as classification accuracy. To determine the direction each weight must be changed in order to come closer to a desired output, the partial derivative of the cost function is computed with respect to the network’s weights . ExampleWithin the past decade, deep learning algorithms have shown super-human capabilities in competitive tasks such as Go and Chess . While tProtein crystal structures have been instrumental in current research surrounding protein–protein interactions, protein function, and drug development. While the number of experimentally determined protein crystal structures has grown significantly, it is dwarfed by the amount of sequence data that has been made available . In the Current methods of protein structure modeling include homology modeling and ab initio modeling. In homology modeling, the sequence of the protein is compared to those of proteins with known structures. Closely related proteins or protein domains are used as structural templates for the corresponding region in the target sequence . Ab initAntibody therapeutics are designed against a target protein. Therefore, it is critical to be able to understand and infer the binding behavior between the antibody and the target, such as whether or not the two proteins will have an energetically favorable interaction (interaction prediction), which residues will form the interaction interface and in what conformation (docking), or how certain amino acid substitutions will change the binding energy (affinity maturation). Docking algorithms attempt to solve the exact three-dimensional conformational pose between two or more interacting structures. Software to predict bound complexes of drug candidates has existed as far back as 1982 . OriginaAnother set of algorithms, named affinity maturation after the similar process in B-cell response, attempts to determine if mutations or modifications to the binding partners have an impact on binding affinity or energetic favorability or generate mutations or sequences which increase the binding affinity of the partners.Target identification includes methods used to locate binding sites on proteins in the absence of knowledge about the protein’s binding partner. Since proteins exhibit specificity towards binding partners, this task is considerably difficult.Antibody binding sites (epitopes) can be classified into two categories T-cell epitopes and B-cell epitopes. B-cell epitopes can further be divided into linear and discontinuous . While TTraditional approaches to these problems have tended to rely on theoretical energy functions (physics-based) or statistical (knowledge-based) energies. However, no force field fully captures the complex interactions present with biological molecules and physics-based simulations require immense computational time. Deep learning offers the ability to perform efficient high-level feature-extraction of properties which are not otherwise captured with current energy functions.In recent years, solutions to computer vision problems have seen significant increases in success due to geometric deep learning and convolutional neural networks ,23. ConvAdditionally, recurrent neural networks—often used in language processing tasks—can be utilized by framing sequence data as a language with a unique vocabulary . RecurreThe application of these similar deep learning methods across multiple problem domains can be attributed to their ability to learn underlying representations. In an abstract sense, disregarding fine-details of the underlying architecture, a naive and untrained deep learning model only becomes domain specific when fit to a set of data. However, this flexibility and reliance on data can render a model sensitive to unintended signals—as seen in adversarial attacks .Efforts to improve antibody modeling have primarily focused on determining the structure of the CDRs from their sequence alone. Modeling algorithms, such as homology modeling, have been largely successful at determining the structure of non-H3 CDRs, which mostly fall into canonical structural clusters, determined by length and amino acid sequences for key residues. Machine learning methods such as Gradient Boosting Machines (GBM) and Position Specific Scoring Matrices (PSSM), have been used to learn how to group and classify non-H3 CDRs into structural clusters ,28. The d, using Cβ atoms and Cα for glycines) and inter-residue orientation angles  by generating probability distributions between pairs of residues. The purpose of the model is to look at hypothetical structures of an H3 loop generated by a modeling algorithm, and rank the structures to identify the most likely conformation of the H3. The benchmark dataset came from the PyIgClassify database  and included only H3 s from humans and mice . Br. Br45]. Five different datasets were used to benchmark the aforementioned methods. In every case, DRREP showcases the best performance amongst all deep-learning methods and best performance amongst all other predictors except for the Support Vector Matrix (SVM)-based LBTope on the AntiJen datasetThe AB-bind dataset is a collection of 1101 mutations for 32 different antibody–antigen structures and includes experimentally-determined changes in binding free energy associated with each mutant, as well as the experimental conditions under which each was tested . While tAntigenDB is a database of validated antigens containing structural, sequence, and binding data . In contAnother antigen data resource is the AntiJen database which is a curated dataset of B-cell and T-cell antigens with experimental annotations, links to published experimental articles, and PDB entries .The Continuous Automated Model EvaluatiOn (CAMEO) platform complements CASP by conducting fully-automated blind prediction assessments each week . The chaThe Critical Assessment of PRedicted Interactions (CAPRI) is an annual competition in which participants are invited to submit structural modeling predictions of protein interactions for unpublished crystal structures . Teams aThe Critical Assessment of Techniques for Structure Prediction (CASP) is a biennial competition in which participants are invited to submit structural modeling predictions  for unpublished crystal structures . Teams aDOCKGROUND is a benchmark dataset of protein–protein complexes, along with generated decoys from the docking algorithm GRAMM-X . The datThe IEDB contains information on sequences, experimental data and in some cases structure data for 973,072 linear and discontinuous epitopes . A curreContains protein complexes for which bound and unbound structures are solved, which have an X-Ray structure with resolution <3.5 Å, and which are non redundant . A breakThe Protein Data Bank is perhaps the largest database of structurally resolved biological molecules . It is cUpdated annually, the PDBbind database currently contains solved structures for 2594 protein–protein complexes extracted from the PDB, along with binding affinity data for each complex .The PIFACE dataset is a set of unique protein-protein complexes . The cluPPI4DOCK is another protein–protein complex dataset with decoys generated from the docking algorithm ZDOCK . StructuSAbDab is a structural antibody database that is automatically updated on a weekly basis and includes annotations such as affinity data, and other antibody specific annotations such as CDR classifications .The SKEMPI database is a curation of mutations of proteins which have been structurally resolved and includes changes in binding free energy. As of this writing, the database includes 7085 entries .As mentioned throughout this paper, an emphasis should be placed on treating antibody–antigen interactions as special cases of protein–protein interactions. Some studies reinforce this point directly by providing comparison of their models between general protein and antibody-specific dataset. Others reinforce this point indirectly, such as with the MaSIF method, which shows that protein interfaces which possess smaller hydrophobic regions (characteristic of antibody–antigen interfaces) are more difficult to classify ,56. HoweThough there exist several commonly used datasets for each problem domain, not all methods utilize a single common set. Furthermore, several methods use a refined form of a given dataset or different protocols for clustering and splitting of training, validation and testing data. These differences make it difficult to arrive at exact comparisons between methods. We hope that the curation of the databases mentioned in this paper will bring the field closer to the status of that in other fields such as that of image recognition. As the amount of data and methods continues to grow, initiatives to maintain benchmarks—such as CASP and CAPRI—will become increasingly valuable.Deep learning requires a significant amount of data. This is demonstrated by its superior performance in domains where large amounts of data are available. While there is indeed a large volume of protein sequence data currently available, structural data still lags behind—due to both the expense of solving protein structure experimentally, and the difficulty of extracting structure from sequence. As antibody–antigen structures are a subset of protein complexes, the amount of antibody structure data is even more limited. Though not applied in any of the aforementioned methods, one strategy to overcome limited antibody–antigen data would be to utilize transfer learning. Transfer learning is the process of retaining knowledge gained during learning from one problem area to another . For exaDue to their evolution, many proteins share structure and sequence similarities. Tools and algorithms—such as BLAST or HMMER for sequence similarity and FATCAT for structure similarity—provide a means to score (and therefore cluster) proteins based on similarity metrics ,63,64. WFurthermore, affinity measures are sensitive to experimental setup which can differ from experiment to experiment. This can be problematic as this information is often not included in model inputs used to predict these values.One hurdle that must be overcome is the generation of meaningful negative data for classification problems. For example, with regard to target selection: while protein–protein interfaces are well conserved—and, therefore, non-interacting residues may be reliably labeled as such—antibodies theoretically have the ability to bind virtually any region on a target protein. Because of this, negative labeling is a much trickier problem. In other words, lack of data showing antibodies binding to a certain region is not sufficient evidence that it is impossible for an antibody to bind that region. This means that labeling antigen residues which have not yet been found to interact with any antibody as definitively “non-interacting” could result in a categorical error. Furthermore, even methods which aim to classify interfaces or complexes as binders or non-binders must still be careful about how the negative structures are generated. For example, DeepInterface’s use of docking decoys means that any freedom from out-of-distribution predictions is reliant on the quality of the docking decoys. That is to say, poor docking conformations may be easily separable from truly-binding conformations, and therefore extensive sampling of the virtually infinite negative space is necessary. However, techniques such as those used in MaSIF, where the spatial orientation of two complexes with respect to one another can be wholly ignored, are not subject to the same risk. As the field of deep learning continues to stretch into other domains, novel architectures and means of representing input data are likely to follow. These domains will bring with them new tasks which can be reformatted and made relevant to biological applications. For example, the triplet loss function used in the MaSIF method was previously used for the task of face recognition . As for In summary, to adapt these biological data for use with developing architectures, one must first establish a compatible representation which maps into an appropriate format, and critically assess what patterns within the representation may be captured by a deep learning framework.Particularly with structure-based methods, the choice of data representation scheme plays an important role in applying a deep learning approach. The growing field of geometric deep learning supplies a number of examples where the representation of structural data in coordinate systems with a local basis both reduces model complexity, and provides invariance to transformations such as rotation. Such methods also circumvent issues that tend to arise in voxel-based techniques, such as data sparsity (most voxels contain empty space) and rigid constraints on the input (such as bounding size of the voxel grid).Crucial to developing and improving upon new methods in any deep learning field is the availability of a common benchmarking dataset. For example, deep learning applications in small molecule design benefit from commonly used benchmarking datasets such as ZINC and ChEMBL and image-based deep learning methods are often evaluated on MNIST or ImageNet ,70. SuchA deep learning methodology from the field of small molecule discovery and design which has not yet extended to biologics is the application of sophisticated generative frameworks for structure generation. Several methods have been proposed which reliably generate novel stable chemical structures and even allow for the enforcement of particular chemical properties, but these are not applicable to proteins. This difference can be attributed to the larger size and number of complex variations protein structures take on, as compared to small molecules. The development of generative models applicable to biologics could lead the way to networks which are capable of designing paratope structure and composition based directly on a given epitope. Not touched upon in this review are developments within the intersection of deep learning and molecular dynamics. In short, molecular dynamics simulations aim to reproduce the movement of atoms as they are manifested in reality. Applications of molecular dynamics are broad and revolutions within the field could accelerate discoveries and developments across many protein engineering tasks. For details covering deep learning advances in this area, the reader is directed to a review article on the topic of machine learning and molecular dynamics .This paper discusses several methods used to improve our understanding of the biochemistry of proteins using the vast trove of biological data which has been created in the past few decades. Deep learning has become an integral part of scientific research in biochemistry at large and is increasingly a component of drug discovery efforts. The growth of biologic drugs—and more specifically, antibodies—as a subject of study makes clear the necessity of adapting deep learning methods to improve outcomes for drug discovery efforts in this field. Antibody drugs have the capability to bind their clinical targets with both high affinity and specificity, and the need for machine learning algorithms specifically adapted to the understanding and enhancement of these proteins is evident.However, a major challenge remains with regard to the amount and type of protein data available. While there are indeed a large number of protein structures available, the volume of data is completely dwarfed by the proliferation of sequence data made available by modern sequencing technology. The development of new protein structure solving methods may improve this disparity, but for now the comparatively small volume of structural data limits the effectiveness of some deep-learning approaches. Additionally, negative data can be difficult to find—especially for problems like binding prediction, where traditional methods of structure determination can only produce positives. Computationally generating such negative data comes with its own set of challenges; it is difficult to know if generated negatives are indeed true negatives, and it is just as difficult to generate negatives that look sufficiently realistic to begin with. Even within the bounds of well-understood positive data, the disparate nature of the various training datasets used by each of these models makes them difficult to compare and makes it difficult to determine which is optimal for a given application.As new data and methods arise to confront the current limitations of deep learning and its application to protein science, we will continue to see incremental advancements in our understanding of this challenging space. The translation of these improvements into new understanding in antibody drug development will be vital to the creation and improvement of new medications. The combination of deep learning with antibody science has boundless potential to aid in the creation of new, sophisticated medicines that push the boundaries of current technology.