AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed. These two events have drawn great attention to AF2 in the science community. AF2 represents a milestone advance in protein structure prediction. It is considered as the greatest contribution of artificial intelligence (AI) to the scientific field and one of the most important scientific breakthroughs made by mankind in the 21st century. This is a very remarkable historical achievement in the human understanding of nature. The high appraisal to AF2 is not excessive because understanding the three-dimensional (3D) structures of proteins is one of the most challenging issues in the field of biology, which has puzzled scientists for 50 years.4 Although multiple technologies including nuclear magnetic resonance (NMR),5 X-ray crystallography,6 and cryo-electron microscopy (cryo-EM)7 have been adopted to solve the protein structures, only about 200,000 proteins’ structures have been determined (https://www.rcsb.org/), covering less than 0.1% of the protein universe.In December of 2020, AlphaFold2 (AF2),AF2 is expected to have a significant influence on the fields of biology and medicine, and may change the way we do related researches such as structural biology, drug discovery, protein design, etc. Despite that the time is short since AF2 was developed, there are already many studies related to AF2 reported. To better understand AF2 and promote its applications, we will in this review paper summarize the algorithm and working principle of AF2 and recipe of its success, particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed. The remaining part of this paper is organized as follows. We will firstly give a brief introduction to the protein structure prediction, followed by analyzing the principle and architecture of AF2 and the secret of its success. Then we will summarize the applications of AF2 in the fields of biology and medicine, and discuss limitations of current AF2 prediction. It will end in concluding remarks.8 raised the famous thermodynamic hypothesis of protein folding (“Anfinsen’s dogma”) that a protein’s native structure stands for a free energy minimum determined by its amino acid sequence, or in other words, the 3D structure of a protein is only determined by its amino acid sequence. This hypothesis is the theoretical foundation of protein structure prediction. Since then, people began to look for algorithms to directly predict 3D structures of proteins from amino acid sequences. In the field of protein structure prediction, CASP, founded in 1994, is a milestone event.11 This competition is held every two years. The CASP committee publishes the “target sequences” globally, for which the experimental structures are known but not yet released. Each participant team that registers for the competition will predict and submit structures of proteins corresponding to the “target sequences” by using their own algorithm within a specified period. Finally, the CASP committee will assess their predicted structures by comparing with those experimentally solved. The competition is double blinded: participants have no access to the experimental structures and referees do not know who make the submissions. Because of the objectivity and fairness, the CASP competition has a very high reputation in structural biology and computational biology communities.In 1961, Anfinsen17 Despite vastly different, they can be roughly grouped into three major classes: homology modeling, de novo modeling, and machine learning (ML) -based modeling.Until now, many algorithms for protein structure prediction have been reported and readers can refer to several recent review papers.(1) Homology modeling19 The homology modeling method mainly uses two techniques: sequence alignment and molecular modeling. The basic workflow of homology modeling is as follows: Given a target amino acid sequence, the first step is to look for its homologous sequences from structure-known protein databases, followed by sequence alignment. Then, coordinates of amino acids of the structure-known homologous proteins are taken as the coordinates of the corresponding amino acids of the target protein. Subsequently, molecular modeling is performed to relax the unfavorable interactions between amino acid pairs. Finally, the generated 3D structure is evaluated.Homology modeling, also known as comparative modeling or template-based modeling, is based on the hypothesis that proteins’ 3D structures are more conserved than their amino acid sequences, and that therefore similar amino acid sequences should have similar 3D structures.24 Advantages of the homology modeling include simple algorithm, fast prediction speed, and high accuracy for proteins that have structure-known homologs. The defect is that it strongly depends on the template structures, which means that it cannot predict structures of proteins whose homologs’ structures have not been determined.25The homology modeling method is the most popular approach decades ago.(2) De novo modeling26 Unlike the homology modeling, the de novo modeling does not depend on the known protein structures, but generating the 3D structure of a target protein only based on the established laws of physics (quantum mechanics). In brief, a de novo modeling method conducts conformation search guided by a designed energy function with the atomic coordinates of amino acids as variables. Many possible conformations are produced in this process and that with the lowest energy is picked. Obviously, the de novo modeling method depends on two factors: (1) an energy function that represents the free energy of target protein with respect to the atomic coordinates of amino acids; (2) an effective conformational search algorithm that can quickly identify low energy states.De novo modeling is a protein structure prediction method based on the “first principles”.33 The advantages of de novo modeling include: (1) it does not rely on the known protein structures, which means that it is able to predict protein structures where no any prior structural knowledge exists; (2) it has the possibility of finding new protein structural types. Nevertheless, this method faces two major obstacles. The first one is the free energy function. Theoretically, accurate calculation of free energy needs to solve the Schrödinger’s equation, which requires huge amount of calculation that we cannot afford even now. Therefore, empirical formulae have to be used. Currently, a majority of empirical formulae are based on molecular mechanics or Newtonian mechanics. The second one is the conformational space of protein, which is an astronomical number. The possible conformational number of a protein with several hundred amino acids is estimated to be about 10300.34 Although great progresses have been made in conformational search algorithms, as well as computing power and storage space, de novo modeling is still only applicable to small proteins with the number of amino acid residues ranging from 10 to 80.There are many investigations regarding protein structure prediction based on de novo modeling.(3) ML-based modeling35 Convolutional Neural Networks,36 Generative Adversarial Networks,37 Transformer,38 etc.). DL has demonstrated its great power in computer vision,39 natural language processing,40 auto-driving,41 and other fields.46 Recently, DL has also been applied to the protein structure prediction.12 At present, there are many modeling methods based on DL, among which AlphaFold,47 RoseTTAFold,48 ESMFold49  and the recent language model by Chowdhury et al.50 are the most famous ones.ML-based modeling is a strategy that utilizes ML algorithms and known protein structures to predict the structures of target proteins. Despite many ML algorithms, the most noteworthy is deep learning (DL). DL has achieved rapid development in recent years, which was driven by the fast growing of data volume (“big data”), a large increase in computing power  and the continuous optimization of DL algorithms 53 value, which is a multi-scale metric to indicate the proximity of the Cα atoms in a model to those in the corresponding structure determined by experiments. The GDT_TS values were calculated respectively according to proteins with different target difficulties: “easy”, “medium”, and “difficult”; an “easy” target implies a protein whose structure is easy to be predicted, for example, a well-folded protein with no loop and structures of its highly homologous proteins being available, while a “difficult” target implies a protein whose structure is difficult to be predicted, for example, a protein with some un-folded domains or many loops, and structures of its homologous proteins being not available. As shown in Fig. Compared with homology modeling and de novo modeling, the DL-based method is a data-driving approach and is the latest emerging one. Due to the great success of DL in other fields, the DL-based protein prediction approach is expected to have a better performance. Indeed, the DL-based method lived up to people’s expectation and won the champion of CASP13AF2 is the most advanced protein structure prediction method of DeepMind. Its principle is based on the state-of-the-art DL algorithms as well as the conservation of protein structures in evolution. It uses a new end-to-end deep neural network which is trained to generate protein structures from amino acid sequences, by utilizing information of homologous proteins and multiple sequence alignments.38 plays a critical role in improving AF2’s performance. Transformer is a newly emerging deep neural network, which applies the self-attention mechanism to obtain intrinsic features and displays great potential of broad applications in AI. Transformer38 was first applied in the area of natural language processing (NLP). It is composed of an encoder module and a decoder module with several transformer blocks of the same architecture. Each transformer block is composed of a multi-head attention layer, a feed-forward neural network, shortcut connection and layer normalization.In AF2, some new DL algorithms developed recently are used, of which attention mechanism-based transformerThe conservation of protein structure in evolution is the biological principle behind AF2. A protein is often conservative in evolution, and the evolution is mostly neutral, which means that most of the mutations don’t affect the protein function. More importantly, protein structure is more conservative than its amino acid sequence. Typically, for example, for a sequence that change by 80% between distant species, the 3D structure may remain almost the same. Conservation of a position in alignment usually implies its importance for protein folding or function. Co-evolution of two amino acid residues of a protein often implies interaction between those amino acids. This information has been used as the basis for 3D structure prediction in AF2.47 A detailed description for the architecture of AF2 can be found in reference.1 Here we present an overview to the architecture and work principle of AF2. As shown in Fig. 54 detected in MSAs has been utilized to assist the protein structure prediction in AF2.The first one is the input module. Given an amino acid sequence, AF2 finds its homologs in sequence databases and conducts MSA by aligning the input sequence and its homolog sequences. AF2 also checks whether any of the homologs has a 3D structure available in protein structure databases, and constructs a pairwise distance matrix between amino acids. Then AF2 generates MSA representation and pair representation. It should be noted that, although both AF2 and homology modeling use MSA, AF2 extracts and utilizes the co-evolution information from the MSA, but homology modeling does not. Intuitively, when two residues (A and B) are spatially near to each other in the folded structure, the mutations of residue A may provoke a selective pressure for residue B to mutate. Such co-evolutionary information55 Uniclust30,56 MGnify57 and BFD (Big Fantastic Database);1 BFD is a database constructed by the team themselves. Pertaining to the structure databases used for training and as templates, it adopts PDB and PDB7058 respectively. AF2 also utilizes several efficient search algorithms, including JackHMMER59 and HHBlits60 for genetic searching, and HHSearch61 for template searching.It is necessary to mention that AF2 uses many high-quality protein sequence databases, including Uniref90,The second one is the Evoformer module, which is likely an encoder. In this module, AF2 takes the inputs (MSA representation and pair representation) from the first module and passes them through a deep learning module . Evoformer produces processed MSA representation and pair representation. The key benefit of using Evoformer blocks is that they are able to switch information between MSA representation and pair representation: the MSA information can be reinterpreted as the pairwise information is improved, and in the similar way, the pairwise information can be further improved as the MSA information is reinterpreted.The Evoformer contains 48 blocks with weights not shared. Each block has two inputs: an MSA representation and a pair representation. The outputs from each Evoformer block are an updated MSA representation and an updated pair representation Fig. . The MSAEach Evoformer block Fig.  containsThe second pathway of transformer-based layers acts on the pair representation. The key feature of this network is that attention is arranged in terms of triangles of residues, which is based on the straightforward principle that in a triangle, any two edges can affect the third edge. The intuition here is to enforce the triangle equivariance. As shown in Fig. The third one is the structure module, likely a decoder. The structure module also uses a transformer neural network. It achieves the transition from abstract representation of protein structure to 3D atom coordinates of target proteins. The structure module takes each residue as a separate object and predicts the rotations and translations required to place it.AF2 adopts an architecture that is completely different from that of previous DL models, including AlphaFold1.The structure module has three input elements. The first one is the single representation containing the abstract information of the target sequence. It is a linear projection of the MSA representation’s first row. The second one is the pair representation output from the Evoformer module. Backbone frames serve as an additional input. Each residue is represented as a triangle, where its vertex next to obtuse angle is Cα atom, and two other vertices are N atom of amino group and C atom of carbonic acid group. The backbone frames, which are the main part of the system prediction, are formed with triangles of the whole amino acid sequence. At the beginning of the structure module, all the backbone frames are placed at the same point in the same orientation. The structural module’s output is the 3D coordinates of all the protein atoms.62 with Amber99sb force field63 is used for the process.The structure module has 8 blocks with shared weight. Each block Fig.  updates Finally, AF2 adopts recycling mechanism for three times to process iterative refinement of training and testing; the recycling mechanism has been broadly utilized in computer vision, which allows the network to be deeper and to process multiple versions of the input features without significantly increasing the quantity of parameters or training time. In each recycling, the model incorporates the previous outputs as additional inputs. AF2 recycles the predicted backbone atom coordinates from the structure module, the output pair representations and the first row of MSA representations from the Evoformer.AF2 has achieved the best performance compared to previous models. Although we have presented the principle and architecture of AF2, the secret of AF2’ success is not explicitly indicated. Here, we present our analysis on the most critical points leading to the success of AF2. From the technological point of view, it is indisputable that the delicate algorithms used are the major causes. Of the most importance is the use of attention mechanism-based transformer. In AF2, several types of attention mechanisms are used, with each one focusing on a specific aspect for the model to learn. In the encoder part, AF2 uses two groups of transformers which are intertwined with each other: one mainly operates on the raw MSA and the other one mainly operates on pairwise information, which update each other through specific information channels between them. The MSA row-wise gated self-attention allows the model to capture long-range dependencies in amino acid sequences and protein structures. The MSA column-wise gated self-attention is a kind of ‘conservation-aware’ attention mechanism, which lets the elements exchange information among species. The triangular self-attention module in the decoder enables the model to learn geometric restrictions within the protein molecules. In the decoder part, AF2 also employs a transformer to geometrically encode residues as a cloud of oriented reference frames in 3D space.64 They used a combination of PDB and a new self-distillation unlabeled data set of predicted protein structures as the training data to train AF2, among which, 25% of the training example comes from the known structures in PDB while 75% of data was from the new self-distillation data set. The aim is to make AF2 recap the protein structures predicted previously challenging by using different training data augmentation methods. This integration data set approach makes use of the data predicted by AF2 and largely improves the performance of the model.The training method is also a factor which makes AF2 success. The designers utilized the idea of self-distillation.Other algorithms or tricks that may contribute to the success of AF2 include the use of recycling approach, end-to-end framework for learning from protein data, and so on. Moreover, big data of amino acid sequences and structures also contribute a lot to AF2’s success. The complete sequence library and sufficient number of single domain protein structures allow deep learning neural networks to explore various dependencies in protein sequence and structure, which could be another important intrinsic cause for the success of AF2.The excellent performance of protein structure prediction by AF2 and the release of structures of more than 200 million proteins are reshaping structural biology, and hence will profoundly impact the fields of biology and medicine that require protein structural information. AF2 and its predicted protein structures will enable researchers to have more opportunity to solve problems that are previously thought to be highly challenging. We will in the follows review the progress of applications of AF2 in the fields of biology and medicine. These applications are classified into eight categories: structural biology, drug discovery, protein design, target prediction, protein function prediction, protein-protein interaction, biological mechanism of action, and others Fig. .Fig. 3Ap65 Rather than saying that AF2 may make structural biologists unemployed, we prefer to the viewpoint that the AF2 and its predicted structures will change the way we do structural biology, including X-ray crystallography, cryo-EM, and NMR spectroscopy. Firstly, predicted structures could be utilized as templates for molecular replacement in solving X-ray crystal structures, implying that traditional selenomethionine phasing is almost not necessary.67 Secondly, these predicted structures may also be helpful for structure determination of large protein assemblies by cryo-EM, which usually needs structures of the component proteins or their domains as a starting point for fitting to the cryo-EM densities. Thirdly, one could also benefit from the predicted structures in using NMR to solve protein structures.69 Typically, the de novo structure determination of domains or proteins using NMR, which is time-consuming, may be replaced by the AF2 structures. Therefore, the application of AF2 prediction allows to make full use of the advantages of NMR in studying protein folding and dynamics.Undoubtedly, structural biology is the most impacted area by AF2.70 utilized X-ray crystallography and AF2 prediction to determine the structure of the VP8* domain (VP8*B) of VP4, which is a spike protein, in group B rotaviruses. In this study, the authors expressed and purified the VP8*B protein. Then they obtained the crystals of this protein and received diffraction data of X-ray. In the process of solving the 3D structure of this protein, instead of using the traditional selenomethionine phasing method, they used AF2 to generate a suitable search model for molecular replacement. The results showed that the AF2 predicted structure almost perfectly matched the diffraction density. Besides the overall fold, AF2 also successfully predicted the orientation of sidechain with high accuracy, which is very close to that determined by experiments. Of note is that they found a novel fold mode by AF2, which has never been reported in homology proteins.Currently there are already many successful applications in this respect. For example, Hu et al.71 recently revealed a structure of the vaccinia virus DNA helicase, the helicase-primase D5, by utilizing combined cryo-EM and AF2 prediction. The obtained structure of D5 shows an AAA + helicase core, which is flanked by N- as well as C-terminal domains. The structure of D5 predicted by AF2 largely helped the construction of the model. The N-terminal domain, which has a 3.9 Å resolution, forms a well-defined tight ring, while the resolution decreases towards the C-terminus, which still allows the fit of the predicted structure. This structure validates AF2 calculations of a large number of structures of viral helicase associated with D5. Jin et al.72 solved the structure of interleukin (IL) −27 signal complex by using cryo-EM with the aid of AF2 prediction, through which they revealed a new mechanism for the assembly and activation of IL-27 receptor recognition complex. Skalidis et al.73 utilized cryo-EM and AF2 to characterize metabolon-embedded architectures of a 60S pre-ribosome, fatty acid synthase, and pyruvate/oxoglutarate dehydrogenase complex E2 cores. Though cryo-EM 3D reconstructions were resolved at resolution ranging from 3.84 to 4.52 Å by collecting less than 3,000 micrographs of a single cellular fraction, AF2 enabled polypeptide hydrogen bonding patterns discernible at this resolution range. These results proposed an integrated approach, powered by ML, which enables the cryo-EM characterization of native cell extracts.Hutin et al.68 recently evaluated the accuracy of NMR structures and AF2 prediction. They used the program Accuracy of NMR Structures Using RCI and Rigidity (ANSURR), which calculates a protein structure’s local rigidity.74 They compared AF2 predicted structures and those determined by NMR and found that AF2 tends to be more accurate than NMR ensembles. They also found that the NMR ensembles are more accurate in some cases, which tend to be dynamic structures, where AF2 had low confidence. They finally suggested that AF2 could be utilized as the model for refining NMR-structure.Fowler and Williamson76 cryo-EM,79 NMR,80 and multiple methods.85There are also some other similar studies in which AF2 is applied to help structural determination, and some of which combines AF2 and experimental methods to verify if the protein structure is solved correctly, for example, combining AF2 with X-ray crystallography,86 Neglecting less ordered regions from protein sequences is often conducive to designing recombinant proteins for investigations pertaining to structures.Besides structure determination, AF2 prediction can even be applied to the design of expression constructs. They enable researchers to better determine where the starting and ending points of a domain locate in the sequence, and avoid less ordered regions;Drug discovery is one of the major application areas that require protein structure information. Although the confidence level of prediction varies, the AF2 predicted structures still could considerably promote the structure-based drug discovery, especially against protein targets with limited or no structural information. At present, protein structures used in structure-based drug discovery mainly come from the RCSB Protein Data Bank (PDB). However, the number of protein structures in the PDB database are quite limited, which is far from meeting the current vigorous demand for drug discovery. The structures’ release of the entire protein universe is expected to accelerate existing and new drug discovery projects.87 recently used Glide,88 a molecular docking program, to benchmark the performance of virtual screening towards 28 common drug targets, each with a known experimental structure and an AF2 structure. The AF2 structures show comparable performance with experimental structures in terms of the enrichment factor, especially when flexible docking was used. The results clearly show that AF2 structures can completely replace the experimental structures in virtual screening.Zhang et al.89 applied AF2 in their end-to-end AI-powered drug discovery engines, which include a biocomputational platform named PandaOmics and a generative chemistry platform named Chemistry42.90 PandaOmics provides the targets of interest and Chemistry42 is responsible for generating molecules based on the AF2 predicted structures, and the selected molecules are then synthesized and tested in biological assays. Through this approach, they discovered a small molecule hit compound for CDK20 (Cyclin-dependent Kinase 20)91 with a Kd value of 8.9 ± 1.6 μM within 30 days from target selection and after only synthesizing 7 compounds. This compound was the first small molecule targeting CDK20 at that time, and this work is the first demonstration of AF2’s successful application in the early drug discovery process.Ren et al.92 applied AF2 to predict the 3D structure of WSB1 (SOCS-box-containing WD-40 protein), a new potential anticancer target95 with 3D structural information not available. The predicted structure was then optimized by molecular dynamics simulations. The optimized 3D structure of WSB1 was taken as the receptor structure to perform molecular docking to screen for WSB1 inhibitors. Finally, they obtained a number of potential active compounds. Among these compounds, G490-0341 displayed the best stable structure and deserved further research and development.Weng et al.96 identified JMJD899 as a novel oncogene correlated with immunosuppression and DNA repair by bioinformatics analysis. Then they used AF2 to predict the 3D structure of JMJD8 and performed virtual screening to retrieve JMJD8 inhibitors. Liu et al.100 proposed a multi-target drug discovery method and applied this method to drug discovery of therapeutic hypothermia.101 In this study, they first predicted the structure for all related protein targets by using AF2 and RoseTTAFold. After that, they applied molecular docking to estimate the interaction between proteins and drugs, and determined optimal single drugs or drug combinations. Considering the differences in the weights of different protein targets, the approach could refrain from inhibiting beneficial proteins effectively while inhibiting harmful proteins.Liang et al.103Except for the above examples, some researches also showed that the side chain quality modeled by AF2 is not good enough for drug discovery, and some recent studies also found that the docking test based on AF2-predicted structures showed weak enrichment performance.106Other researches with AF2 applied in drug discovery include the literature.109 It is a complex and challenging task, which is mainly hindered by the difficulty in reliable prediction of protein 3D structures from amino acid sequences. AF2 as well as other machine learning algorithms (such as RoseTTAFold and recent language models50) likely removes this obstacle. It is no exaggeration to say that with AF2 prediction, we will step into a new era of protein design. Some typical protein design examples by using AF2 are given as follows.Design of proteins means creating novel proteins with desired structures and functions. De novo protein design is a longstanding fundamental goal of synthetic biology.110 developed a computational framework for de novo protein design that embeds AF2 as an oracle within an optimizable design process. This is an adaptable framework for protein design through sequence optimization utilizing evolutionary algorithms. It extends previous studies towards protein design by leveraging structure predictors.112 The integrity of the structures predicted is validated and confirmed by standard ab initio folding, protein structure analysis methods, and rigorous all-atom molecular dynamics simulations. They also showed a potential application of their method in designing de novo protein monomers, dimers and oligomers, as well as protein binders for target proteins and proteins which change conformation upon complex formation.Jendrusch et al113 designed a pipeline for de novo protein design based on AF2. In the beginning, they just inverted the AF2 model, utilizing a loss function and the prediction weight set to bias the generated sequences for the objective of adopting a target fold. However, as observed in the protein surface’s hydrophilic versus hydrophobic patterning, the approach does not seem to fully capture basic principles of de novo protein design. Then they made modifications to their pipeline system with minimal post-design intervention, and conducted in vitro validation, which demonstrated that some designs were folded and stable in solution in the condition of high melting temperatures. Overall, the revised pipeline generated viable sequences as assessed experimental characterization, showing the possibility of contributing to solving outstanding challenges in the field of de novo protein design.Goverde et al.117 It is also necessary to mention that, relative to AF2, RoseTTAFold has more applications in protein design, largely due to Baker’s groundbreaking work.120Other interesting studies with AF2 assisting protein design include the literature.124 are often expensive and time consuming. Computer-aided target prediction may help narrow the scope of target identification, which is often based on protein-ligand docking, usually called inverse docking. Previously, the inverse docking faces a challenge of lacking 3D structures of all possible protein targets. The AF2 structures provide an unprecedented opportunity to develop feasible target prediction methods.Target prediction, including on-target and off-target identifications, are important not only for understanding physiological and pathological processes, but also for identifying novel drug targets and evaluating selectivity of drugs. Experimental approaches to target identification, such as various activity-based protein profiling (ABPP)-based methods,125 utilized the AF2 structures to construct the first pocket library for all the proteins in the human proteome, called the CavitySpace database. CavitySpace can be applied to identify novel targets for known drugs in drug repurposing or side effect researches. This database can be easily used to the target prediction by inverse docking. The building workflow of database is as follows: they collected 23,391 human proteins from AlphaFold protein structure database and 6956 human reference proteins from PDB. Then, they applied CAVITY, a tool developed by the same research group to detect all the possible cavities on protein surfaces,126 to identify all the potential cavities on protein surfaces. The CavitySpace database is freely available at http://www.pkumdl.cn:8000/cavityspace/.Wang et al.129There are also other related studies applied AF2 for target prediction.Currently, there are still many proteins whose functions are not known or poorly understood. Since 3D structures of proteins completely determine their functionality, this characteristic can be utilized to establish data-driven prediction models of protein function. Nevertheless, the insufficient number of available protein structures severely limits the performance of these models. The structures predicted by AF2 have provided a promising solution towards this problem, and are expected to improve the performance of these models via increasing the amount of training samples.130 recently conducted a comprehensive study to investigate whether AF2-predicted structures could enhance the protein function prediction performance. In this study, they proposed a state-of-the-art structure-based protein function prediction approach and constructed a new benchmark data set. After that, they evaluated whether the performance of the protein function prediction model could be improved by putting additional protein structures predicted by AF2 into the training data set. They further compared the performance differences between two models separately trained with structures predicted by AF2 only and with real protein structures only. Their results demonstrated that protein function prediction models based on structures could benefit from virtual training data composed of structures predicted by AF2. Even, the model trained only using the structures predicted by AF2 achieved comparable performances to the model based on real protein structures, which are solved through experiments. This indicates that the structures predicted by AF2 were almost equally effective in protein function prediction.Ma et al.131 explored the utility of the Protein Language Models (PLMs) module in AF2, Evoformer, in protein function prediction, and particularly compared the performance of evolution-based & evolution-free protein language models as protein function predictors. They showed that evolution-based PLMs performed better than evolution-free models only in the structure prediction tasks, but in general, were worse than evolution-free models in most function prediction tasks. Consistent with structure prediction, evolution-based PLMs are also sensitive to the amount of MSAs when predicting protein function.Hu et al.132 constructed and evaluated 3D feature representations of protein structures using space-filling curves, in which AF2 predicted protein structures were used. In this study, two enzyme substrate predictions were used as case studies: the S-adenosylmethionine dependent methyltransferases (SAM-MTases) and the short-chain dehydrogenase/reductases (SDRs). As their results demonstrate, enzymatic function could be predicted from feature representations on the basis of the 3D structures of SAM-MTAses and SDRs with good accuracy.Interpretable and compact structural feature representations are important for accurate prediction of protein properties and function. In a recent study, Rappoport and Jinich133 binding protein domain) from AF2 predicted structure database, Bartas et al.134 identified 185 proteins with a putative Zα domain, which may bind to Z-DNA/Z-RNA and play an important role in a variety of cellular processes.By searching proteins that contain Zα domain  refers to the process in which two or more protein molecules form a protein complex through non-covalent bonds.139 extended AF2 to the prediction of multiple-chain complex, and the system was named as AlphaFold-Multimer. On a benchmark data set of 17 heterodimer proteins without templates, they achieved at least medium accuracy on 14 targets and high accuracy on 6 targets. They also predicted structures for a large data set containing more than 4,000 recent protein complexes, from which they scored all the non-redundant interfaces with low template identity from these protein complexes. For heteromeric interfaces, they successfully predicted the interface in 67% of the cases, and 23% of the cases were predicted with high accuracy. For homomeric interfaces they effectively predicted the interface in 69% of cases, and produced high accuracy predictions in 34% of cases. All these results demonstrated superior performance compared to existing approaches. The AlphaFold2-multimers has now been used to predict protein-protein complex structures. For example, Gómez-Marín et al.140 applied AlphaFold-multimer for the prediction of PHF14-HMG20A complex models. Ivanov et al.141 also applied AlphaFold-multimer to predict the homodimers structure of CYP102A1.Evans et al.142 applied AF2 to predict heterodimeric protein complexes. In this work, they explored the docking effect by using the AF2 pipeline combined with different input MSAs, which is for studying the relationship between the output model quality and these inputs. Through scoring multiple PPI models with a predicted DockQ score (pDockQ), they could distinguish from incorrect models with high confidence acceptable (pDockQ > = 0.23). They concluded that AF2-based docking outperformed another docking method.143Recently, Bryant et al.144 examined the performance of AF2 in predicting structures of protein complexes from amino acid sequence. They used 152 diverse heterodimeric protein complexes to form a benchmark test data set. In this test, 43% of cases that had near-native models were produced as top ranking prediction results by AF2, substantially outperforming the performance of unbound protein–protein docking method (9%). To examine the effect of AlphaFold_Multimer in predicting antibody–antigen interaction, the authors used a set of antibody-antigen structures, which were released recently. The testing results confirmed a low success rate for the modeling of antibody–antigen complexes. They further observed that, via the algorithm, T cell receptor–antigen complexes are similarly not accurately modeled. These findings demonstrate that AF2 faces challenges in handling the adaptive immune recognition. Gao et al.145 developed an AF2-based system called AF2Complex, which can predict direct physical interactions in multimeric proteins. Contrary to normal approaches, paired MSAs are not necessary for AF2Complex. It improves significantly over AlpahFold-Multimer and reaches higher accuracy compared to some complex protein-protein docking approaches. Moreover, the authors introduced metrics used for direct protein-protein interactions prediction between arbitrary pairs of proteins and validate AF2Complex on the E. coli proteome as well as some challenging benchmark sets.Yin et al.146 suggested an AF2-based strategy to model peptide-protein complex which did not require MSA information for the peptide partner. In this way, binding-induced conformational changes of the receptor could be handled. The outcomes demonstrate that AF2 could be expected to provide structural insight into a broad range of peptide-protein complexes.In addition to PPI, AF2 can also been used in the prediction of peptide-protein interactions. For example, Tsaban et al.153For some similar investigations related to protein-protein interaction or protein-peptide interaction by using AF2, readers can see the literature.Exploring biological mechanism of action is often complicated and remains a challenge. Studies of biological mechanism of action include many aspects, such as drug-target interaction mode, mechanism of biological enzyme catalysis, and so on.102 combined molecular docking simulations with AF2 for protein-ligand interactions prediction. They successfully predicted the interactions between 296 proteins spanning the essential proteome of Escherichia coli, and 218 active antibacterial compounds and 100 inactive compounds, respectively. They measured the enzymatic activity of 12 essential proteins which were treated with each antibacterial compound to benchmark the performance of the model. This research suggests that advanced approaches in modeling protein–ligand interactions, especially utilizing methods based on machine learning, are needed to better leverage AF2 for mechanism of action studies as well as drug discovery.In silico molecular docking methods have been broadly applied to the prediction of drug-target interaction. Nevertheless, this kind of methods strongly rely on existing protein structures. AF2 provides alternative approach to retrieval of accurate protein structures. Wong et al.154 is a highly endergonic reaction. Some anaerobic bacteria conquer the energetic barrier through electron bifurcation/confurcation (FBEB/FBEC) based on flavin utilizing a lactate dehydrogenase (Ldh) combining with EtfA and EtfB, which are the electron-transferring proteins. However, the mechanism of action is poor understood. In a recent study, Kayastha et al.155 utilized AF2 calculations and obtained a plausible new B (bifurcation-connected) state, which allows electron to transmit between the shuttle FADs and EtfAB base. Based on the findings, they put forward an integrated catalytic mechanism of the FBEC process.Lactate oxidation with NAD+ as electron acceptor156 characterized the C-to-Ψ editing mechanisms. They showed that TrcP mediates the stepwise editing of C-to-U followed by the conversion of U to Ψ. The structure modeling based on AF2 revealed a distinct long helical domain within TrcP which possibly binds and orients the substrate tRNA during both reactions, The findings suggest that TrcP mediated C-to-Ψ editing depends on a substrate channeling mechanism. These discoveries offer mechanistic views into an RNA editing process which could possibly stimulate environmental adaptation.Post-transcriptional RNA editing regulates the expression of gene in a condition-dependent manner, which mechanism remains unclear. In a recent study, Kimura et al.157 explored the substrate determinants and recognition mechanism of separase, which is a giant cysteine protease. In budding yeast, they identified a conserved motif downstream of the cleavage site. Using AF2 and molecular dynamics simulations, they discovered that in a conserved cleft near the binding groove of separase’s inhibitor securin, the motif is recognized by separase. The binding is mutually exclusive and requires separase’s conformation changes. Their research could let scientists get a deeper understanding of mechanism of substrate recognition and activation of separase.Liang et al.158 applied AF2 to predict the structure of selected KRAB159 domains. They discovered an evolutionary conserved L-shaped body of two α-helices in all the domains of KRAB. It is changed into a typical spatial arrangement especially for mKRAB-AB after the replacement of amino acid and together with a third helix provided by mKRAB-B. This provides basic insights of how KRAB form complex with TRIM28. McMullen et al.160 found from yeast that EKP-GCSF and GCSF exhibits similar binding to its receptor GCSF-R. Similarly, to study the structural effects of EKP162 on GCSF, they applied computational modeling using AF2 in conjunction with molecular dynamics simulations. Computational modeling shows that EKP does not change the structural behavior of GCSF, which demonstrates that EKP does not hinder receptor binding. Furthermore, the initial conformation of EKP-GCSF from AF2 shows that the EKP which is around GCSF might provide evidence of the thermal-protectivity of EKP on GCSF.Lorenz et al.176There are also other studies pertaining to biological mechanism of action.180 rare disease treatment studies,181 effects of mutation on treatment,187 vaccine design,190 and so on. For example, Tang et al.180 investigated the relationship between organism evolution and protein evolution based on the structures of proteomes from 48 organisms predicted by AF2. They found some interesting phenomena, including: (1) constituent proteins of organisms with higher complexity would have larger gyration radii, higher coil fractions as well as slower vibrations, and (2) higher degree of functional specialization of proteins is associated with higher degree of organismal complexity. This research brings new views about how the proteins’ functionality diversity increases, and how the dimensionality of the manifold of protein dynamics decreases in the process of evolution. Sebastiano et al.181 found that protein structures predicted by AF2 have a potential to assist rare disease treatment studies. In this investigation, the authors focused on Alsin, a protein responsible for rare motor neuron diseases. With the AF2 predicted protein structures, they evaluated the flexibility profile of Alsin and its mutants, and models of dimeric/tetrameric Alsin responsible for its physiological action. They concluded that efforts of drug discovery targeting Alsin-involving diseases should be pursued. Yang et al.184 applied AF2 to predict the S, N, and M proteins’ structures of the Omicron variant of SARS-CoV-2. They analyzed how the S protein and its parts, S1 RBD and NTD, have been affected by the mutations in detail, and also how the current SARS-CoV-2 vaccines and treatments would be affected by these mutations. Zeng et al.188 utilized AF2 to design a hemagglutinin stem vaccine “B60-Stem-8070”. This vaccine showed better performance compared with the original hemagglutinin stem antigen.In addition to the application areas mentioned above, AF2 prediction could also be applied to some other fields, such as protein evolution,The invention of AF2 is a game-changer event in structural biology. It has reformed the field of protein structure prediction by utilizing sequence information to model protein folds quickly with atomic-level accuracy. However, current AF2 was trained on protein structures from the Protein Data Bank in which X-ray crystallographic structures dominate. Therefore, it is best considered as a predictor of the structured state under experimental conditions where a protein is likely to crystallize, other than a predictor of the lowest free-energy state under physiological conditions. This together with some inherent limitations in methods and techniques limits applications of AF2 predictions in many aspects, which are summarized as follows.197 The protein structure predicted by AF2 is a static state. However, proteins are very dynamic with multiple states. Many important physiological and pathological proteins (such as ion channel proteins) have very subtle conformational changes under different active states, and will also show ever-changing spatial configurations due to their combination with various other proteins inside and outside the cell. At this point, AF2 often gives a single optimal solution, which is difficult to cover the conformational diversity of proteins. However, this does not mean that it is not possible to understand protein dynamics with AF2. According to several recent studies,201 AF2 can still be used for some analysis of protein dynamics. For example, Del Alamo et al.199 recently presented a method to drive AF2 for sampling alternative conformations of topologically diverse transporters as well as the G-protein-coupled receptors that do not exist in the training dat set of AF2. Nevertheless, the exploration of the conformational space is in part a by-product of low sequence information which is provided for inference.Protein dynamics is a very important research area.202 Additionally, AF2 could hardly be used for the structure prediction of a protein with multiple domains, such as a transmembrane receptor with a large extracellular domain.203 There is still a demand of new deep learning methods to be designed for the prediction of ensembles of biophysically correlated states.There are also other studies pointing out that AF2’s weak performance in identifying conformational ambiguity.207 it is difficult for existing methods to predict the morphology, dynamics and interactions of disordered regions of proteins in solutions.The AF2 database consists of highly accurate predictions for the folded part of a large number of proteins. Nevertheless, AF2 does not well in predicting structures of proteins, in cases fewer sequences are available for alignment, and regions that are natively unfolded or disordered regions, for example, loops. The loop structures are relatively stable in crystals, but are very flexible in solutions. Although many approaches have been tried,208 which refers to small molecules or peptides that bind to a site of an enzyme protein different from its endogenous ligand binding site to cause conformational changes, thereby changing the activity of the enzyme. Besides allosteric modulators, plenty of orthosteric ligands, which bind to the identical site as the endogenous ligand, can also induce conformational changes. Nevertheless, AF2 is not designed to determine how proteins change their shape in the presence of other interacting ligands or proteins.It has been well known that small molecule ligands or proteins may induce a protein to undergo conformational changes. The most representative example is allosteric modulators,210 it was also observed that the performance of missense mutations prediction is not good in other studies,211 and there were only weak or no correlations between the output metrics of AF2 and changes in protein stability or functionality.212Point mutations are frequently encountered in proteins, particularly pathological state. Understanding the effect of missense mutations on protein structure may help unveil their biological or pathological mechanism. Even though AF2 could predict wild-type (WT) structures, it likely performs poorly in predicting the effect of missense mutations on the proteins’ 3D structures. Although there are researches showing that AF2 could predict the phenotypic effect of missense mutations,215 such as phosphorylation,216 methylation,217 acetylation,218 and glycosylation,219 are common in proteins. These post-translational modifications may lead to conformational changes in protein structures.220 For example, phosphorylation of inactive kinases in their active loop often results in a large conformational change, and eventually activate the kinases. However, AF2 can predict protein structures only based on their amino acid sequence, and post-translational modifications of residues cannot be recognized. Therefore, the conformational changes due to post-translational modifications cannot be predicted with current AF2.Post-translational modifications,51 developed an end-to-end deep neural network model, namely differentiable recurrent geometric network (RGN) model, in which a protein language model (AminoBERT) based on the Bidirectional Encoder Representations from Transformers (BERT)221 was used to learn latent structural information from unaligned proteins. Language models were firstly introduced to extract semantic information from words. The RGN model showed better performance on orphan proteins than AF2, while significantly reducing the computing time by up to 106-folds. These results demonstrate the theoretical and practical strengths of protein language models in structure prediction compared with MSAs.In addition to the above-mentioned limitations, AF2 and other computational systems that use DL and the information of co-evolutionary relationships encoded in MSAs also face challenges in prediction of orphan proteins and artificially designed proteins because an MSA cannot be generated. Recently Chowdhury et al.49 and RGN50) enable end-to-end protein structure prediction directly from amino acid sequences with high speed and accuracy.We finally have to mention that AF2 itself has some limitations in methods and techniques. For example, (i) deep learning models have low interpretability currently; (ii) the AF2 structural prediction is based on the data of MSA, i.e., a large number of evolutionarily related sequences is needed for the structure predictions, which might cause side effects such as comparably slower prediction speed. As a comparison, language models (such as ESMfoldThe excellent performance of AF2 in predicting protein structure together with the release of structures of more than 200 million proteins predicted by AF2 is reshaping structural biology. AF2 will certainly have a significant impact on researches that need protein structure information, and could be applied in many fields such as drug discovery, protein design, target prediction, protein function prediction, PPI, biological mechanism of action, and others, in addition to experimental structural biology. Despite just a very short time since AF2 was developed, we have already witnessed a number of successful applications. We believe that, as time goes on, more applications or new application fields will be developed, for example, design of protein machines with complex or specific functions, design of new organisms, and disease diagnosis. Even so, AF2 prediction is not a panacea and there are many issues still needing to be solved, including protein dynamics, structures of disordered regions of proteins, structures of mutants, structures of protein-ligand complexes, structures of proteins with post-translational modifications, and so on. With the further development of AI algorithm, ever-increasing data, and computing power, it is expected that more surprises will surely come to us in future.Finally, it is necessary to mention that, during the revision of this article, the new CASP competition, CASP15, is over. Different from previous CASPs, which took protein structure prediction as the main track, CASP 15 also paid attention to predicting protein complex and RNA structures. This is consistent with the CASP’s style or philosophy that keeping pace with the times, which means that protein complex structures and RNA structures might be the new focuses in the “post AlphaFold era”. Another noteworthy point is that DeepMind did not take part in CASP15, which reasons are unclear. Nevertheless, all teams that have achieved better results have more or less used AF2 algorithms or AF2 predicted structures, which implies that AF2 invisibly won CASP15 again, further highlighting the great influence of AF2 in structural biology. Overall, we look forward to new breakthroughs of AI in structural biology, and more application achievements by using AF2 in the future.


Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein–protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through  Transmembrane proteins comprise approximately 20–30% of the human genome  and are Most existing computational tools focus only on addressing object-specific issues, such as database handling , featureHere, we present TMKit, a scalable Python toolkit that comprises various function modules for the analysis of transmembrane protein sequences, structures and functions. While many function modules in TMKit are specialized for transmembrane protein-related problems, they can also be applied to other kinds of proteins. One of the standout features of TMKit is its visualization module, which facilitates the biological validation, interpretation and translation of known and predicted PPI interfaces. In addition, TMKit also incorporates seqNetRR, a high-performance computing library that simplifies the construction of residue–residue connections and speeds up feature assignment, enabling faster feature analysis and machine learning modeling. Overall, TMKit is a helpful toolkit for computational studies of transmembrane proteins.visualization, sequence, quality control, topology, mapping, annotation, connectivity, edge extraction and feature  and a list of probabilities of residue sites to be involved in interactions. The program can automatically generate the label- or propensity-based PPI interfaces in between a POI and its interacting proteins (or its larger complex), which can be visualized in PyMOL . For insk. A sliding window of any size applied to each of the single residues or residue pairs can further generate a list of neighbor-surrounded residues or residue pairs in sequence context.The sequence pre-processing module is a fundamental component of TMKit, designed to handle sequence reading in diverse formats, sequence retrieval from various sources and multiple sequence alignment (MSA) generation. It can retrieve sequences from PDB and PDBTM, parse sequences from FASTA, structure and XML files, and stitch discontinuous residues together from PDB structures  [http://pdbtm.enzim.hu/) [Transmembrane protein topologies are vital to their structures and functions , 29. Forzim.hu/)  or PDBTMzim.hu/) . Howeverzim.hu/)  and applzim.hu/) , to rediIdentifier mapping between structural and sequence data (e.g. PDB residue IDs and FASTA residue IDs) is an important technical premise to guarantee the correct interpretation of biological findings. Often, residues in FASTA format are imperfectly aligned with those from the PDB structure, as experimentally resolved residues in its crystal structure can become discontinuous due to various challenges associated with the crystallization process . The mapIn addition, protein structures are rendered as the asymmetric units (ASUs) in their PDB files, which represent the smallest but most fundamental units that can be reassembled to the quaternary structures  through symmetric operations such as rotations and translations based on BIOMT records , 36. An Amino acid residues of transmembrane proteins to be involved in mutations and function domains can be annotated through the MutHTP , Pred-MuStudying connections of a protein to others in a PPI network is of crucial importance to understand its biological role. There are a few well-established databases such as BioGRID , IntAct seqNetRR for extracting residue–residue connections.Quantification and characterization of connections between residues are thought to be key to deciphering and understanding the mechanisms behind a biological network. However, handling a large number of connections is often computationally intractable, primarily due to time complexity involved. Progress in residue contact prediction and interaction site identification has shown that the utilization of neighboring information of residues or residue pairs can result in an improvement in prediction accuracy. We provide a high-performance computing library for extracting connections between residues by constructing bipartite and unipartite graphs (where residue connections are treated as edges) and assigning features in linear time with respect to the number of residues used. We particularly detail this in section ab initio structural modeling [A set of transmembrane protein-specific and general-purpose features is provided by TMKit in support of machine learning modeling. We first incorporate LIPS , a methomodeling , 48 and modeling . MoreoveWe additionally developed seqNetRR, a high-performance computing library for constructing residue connections in sequence and contact-map context and performing coevolutionary feature assignment in linear time. This module is aimed at facilitating feature engineering, scheme design and organization for machine learning modeling of interactions/contacts between residues. Below, we demonstrate the conceptualization, construction and implementation of seqNetRR.Formulation of LocRRCs and GlobRRCs for details) and the serially ordered neighboring residues of the other one residue of the residue pair . It is commonly seen in structural biology that coevolutionary features assigned to GlobRRCs are used to amplify the prediction performance of long-range residue contacts [Connections between a residue of interest and its serially ordered neighboring residues are referred to as local residue–residue connections (LocRRCs for short). As seen in contacts , 50. FigCartesian products  To describe connections between residues, we define a graph where Cartesian products  Similarly, all GlobRRCs are described as binary relations where For LocRRCs, a set of edges The two unipartite graphs vailable  are usedpatch, memconp and cross  for every single residue by summing the correlation coefficients of threshold-based co-evolutionarily important connections with other residues . For exat ratio) . This hat ratio) , 34). Inwhere eContact ) over thx-axis), the second dimension representing the total number of residues/residue pairs of interest (y-axis) and the last dimension storing the sequence positions of every two residues (z-axis). However, this can bring additional computational cost because of the added operation of creating, storing and re-extracting LocRRCs or GlobRRCs. It worsens heavily when large proteins with exponentially increased residue pairs are used. The seqNetRR module of TMKit manages to assign features by directly using the data object in green to bypass the separate generation of the data object in blue. A hash table is built to look up the correlation coefficients  from a correlation matrix. We term this method Hash. Relatively, the feature assignment method that is involved in the separate generation is called Hash_indirec.Efficiently assigning features to a substantial number of residue pairs is a crucial preliminary step before undertaking more sophisticated computational modeling. For a given protein sequence, a list of residues/residue pairs of interest are extracted first to be stored technically as the data object in green . The paired t-tests confirm the statistically significant difference between the running time of every two methods . We included the comparison with the NumPy method because of its acceptable CPU time in handling LocRRCs. Similarly, the issue on the feature assignment to LocRRCs has also turned out to be trivial by using the Hash method, where the running time is only 1.63 s on average . The Hash method is 33-fold and 11-fold faster than the NumPy array indexing method and the Pandas pandas.at method, respectively.Next, we examined the feature assignment to LocRRCs restricted by a 5-node unipartite graph .We finally assessed the computing performance of assigning cumuCCs to individual residues using top-ranked residue pairs of protein lengths Methods) to handle various types of analyses of transmembrane protein sequences and structures. The TMKit functions can be accessed via the following modules: tmkit.cath, tmkit.collate, tmkit.edge (i.e. seqNetRR), tmkit.feature, tmkit.mapping, tmkit.msa, tmkit.mut, tmkit.ppi, tmkit.qc, tmkit.rrc, tmkit.seq, tmkit.topo and tmkit.vs. They are mainly used inline in Python. Note that in order to facilitate the development of TMKit, many functions provided by some of these modules work based on a few external Python packages or libraries, including LIPS [TMKit is designed in an open-source, modular and scalable manner with 13 Python modules  provides the specification of the 13 Python modules where case studies are presented (The TMKit website (resented . Each caIn this work, we present TMKit, a computational toolkit that offers numerous function modules for analyzing transmembrane protein sequences and structures. Many of its modules facilitate machine learning modeling of transmembrane proteins. TMKit’s visualization capabilities allow atomic-level visualization of known and predicted protein–protein interaction interfaces for rapid biological interpretation. In addition, TMKit integrates a high-performance computing library seqNetRR, which can construct various sets of residue connections and assign features in linear time with respect to the number of residue pairs used. The seqNetRR module is designed to permit fast computation for learning the surrounding information of residues/residue pairs of interest in machine learning modeling problems.TMKit distinguishes itself from existing tools mainly in terms of its easy-to-use functions, fast running speed and relatively comprehensive coverage of a wide range of capabilities that handle transmembrane protein data in different formats. Transmembrane protein structures are routinely collected into a few bespoke databases, such as PDBTM . Given tThe relentless progress in computational techniques has transformed the landscape of the entire structural biology field . For exaTMKit is the first Python interface for providing a one-stop computational analysis of transmembrane protein sequences and structures.TMKit offers a range of flexible functions for visualizing atomic-level interaction/mutation interfaces between transmembrane proteins.TMKit includes a direct module to access many transmembrane protein-specific databases, e.g. PDBTM, for fast database wrangling.TMKit is well suited for extracting biological features of single/paired residues from large transmembrane proteins at a fast speed.TMKit allows for detailed topological, mutational and interaction annotations of transmembrane protein sites.Supplementary_File_bbad288Click here for additional data file.


This would help design mitigation strategies for high altitude illnesses. In spite of a number of studies spanning over 100 years, still the complex mechanisms controlling acclimatization to hypoxia remain largely unknown. To identify potential diagnostic, therapeutic and predictive markers for HA stress, it is important to comprehensively compare and analyse these studies. Towards this goal, HighAltitudeOmicsDB is a unique resource that provides a comprehensive, curated, user-friendly and detailed compilation of various genes/proteins which have been experimentally validated to be associated with various HA conditions, their protein–protein interactions (PPIs) and gene ontology (GO) semantic similarities. For each database entry, HighAltitudeOmicsDB additionally stores the level of regulation (up/down-regulation), fold change, study control group, duration and altitude of exposure, tissue of expression, source organism, level of hypoxia, method of experimental validation, place/country of study, ethnicity, geographical location etc. The database also collates information on disease and drug association, tissue-specific expression level, GO and KEGG pathway associations. The web resource is a unique server platform that offers interactive PPI networks and GO semantic similarity matrices among the interactors.These unique features help to offer mechanistic insights into the disease pathology. Hence, HighAltitudeOmicsDBis a unique platform for researchers working in this area to explore, fetch, compare and analyse HA-associated genes/proteins, their PPI networks, and GO semantic similarities. The database is available at  Some lowland residents adjust to the reduced oxygen availability at high altitude through a process known as acclimatization but some suffer from various disorders like Acute Mountain Sickness (AMS), High-Altitude Cerebral Edema (HACE), and High-Altitude Pulmonary Edema (HAPE) etc.3. Therefore research for the identification of the early signs of these physiological alterations is gaining momentum. A recent comparison in protein profiles of low-landers with their induction at high altitude has identified differentially expressed proteins like serum proteins Irisin, Myostatin, Acute Precursor Proteins (APPs), Apolipoprotein A1 etc. during HA acclimatization4. These proteins are associated with energy-related processes, skeletal muscle regeneration, inflammatory responses, and other hallmark molecular responses at high altitude6. Henceforth these proteins were proposed as biomarkers to predict early acclimatization of individuals at high altitude. Hunting for novel protein biomarkers in low landers and native samples using peptide profiling has become an important method to identify potential diagnostic or therapeutic markers6. Identification of the differentially expressed proteins that play a key role in the acclimatization process has helped to uncover the mechanisms responsible for the acclimatization at HA.A genome-wide study has uncovered plasma proteins that have the potential to predict vascular homeostasis during HAPE7. Similarly, a transcriptomic study indicated the modulation of multiple pathways and proteins involved in the early phase of hypobaric hypoxia exposure like VIM, CORO1A, CD37, STMN1 etc.8. Though there is enormous literature available that have reported ‘-omics’ profiles of humans and animals exposed to high altitude; the real challenge remains to integrate all these studies to produce a holistic understanding of continuously evolving mechanisms involved in functional adaptations of cells, tissues and organs, as well as the whole organism in the high-altitude hypoxic environment. Hence, we developed HighAltitudeOmicsDB where all this scattered data is collected, curated, analyzed, and visualized. The database currently contains ~ 1300 protein associations that have been manually curated from peer-reviewed publications which have been experimentally proven to be regulated by HA stress. The database stores the association of each protein with HA-stress in terms of the level of regulation (up/down-regulation), fold change, the study control group, duration and altitude of exposure, tissue of expression, source organism, level of hypoxia, method of experimental validation, place/country of study, ethnicity, geographical location etc. The database also provides whether the protein has been experimentally proven to be associated as a HA-biomarker and provides a link to the corresponding publication. The database is also cross linked to other databases like such as protein official symbol, protein aliases, chromosomal location, length, Uniprot ID, Enzyme Commission (EC) Number, Protein Family Information (Pfam) ID, Protein DataBank (PDB) ID, The Integrative Protein Signature Database (InterPro) ID, Single Nucleotide Polymorphism Database (dbSNP) Id. The database also presents protein’s functional information like GO annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways association; their association with other diseases and drugs. The database also provides protein–protein network interactions of each protein with its top-50 interacting partners. The network can be visualised interactively on the webserver. Additionally, HighAltitudeOmicsDB calculates gene semantic similarity with these 50 interactors to identify functionally related proteins. The database additionally stores the transcription factors interacting with the gene and their regulation type . Additionally, the miRNAs interacting with the gene is also listed. Thus, HighAltitudeOmicsDB is a unique integrated platform to explore, retrieve, compare and evaluate genes/proteins associated with HA-stress, their PPI networks and semantic similarity and regulation by transcription factors and miRNAs. This will help uncover the underlying crosstalk between proteins that exists to acclimatize to HA and also provide mechanistic insights in these complex molecular responses. It will thus be useful in identifying novel and robust molecular biomarker candidates that can further help in the development of new diagnostic, prognostic and therapeutic strategies for high altitude disorders.A large percentage of the world’s population lives at High-Altitude (HA) areas and many also visit the mountains above 2500 m for outdoor activities such as trekking, climbing, and other adventure sports. Rapid ascent to high altitude leads to an instantaneous decrease in barometric pressure. The oxygen concentration remains the same but the number of oxygen molecules per breath is reduced; e.g. at an altitude of 3600 m, the barometric pressure decreases to 483 mmHg and < 40% of oxygen molecules are available to breathe.Since the amount of oxygen required for activity is the same, the body must adjust to having less oxygen or hypobaric hypoxia9. The publications were manually scrutinised to identify differentially expressed genes/proteins. After removing redundancy and duplicity, a comprehensive list of proteins that have been found to be Differentially Expressed (DE) at HA were curated from these publications. For each DE protein, its associated information was also fetched that includes; ‘Name of the protein’, ‘Protein Official Symbol’, ‘Aliases’, homologous ‘Human Entrez ID’, ‘Source Organism’, ‘Tissue of expression’, ‘level of hypoxia’, ‘altitude’, ‘duration of experiment’, ‘Level of regulation’, ‘Fold change’, ‘Experiment details’, ‘geographical location’, ‘ethnicity’, ‘Control group’, ‘Associated as Biomarker’. Studies in which the source organism was other than human, the homologous human gene/protein was identified using protein BLAST against the Uniprot Database. The homologous human protein with the highest sequence similarity and least E-score was selected. The minimum threshold was considered as > 80% pair wise sequence similarity. This way, even for experiments conducted on different experimental organisms (mice/rats/yak/bird/toad/sheep), human equivalence/translation would be easier.The collection was stored in JavaScript Object Notation (JSON) file format and stored in MongoDB10.A combination of various keywords such as “high altitude”, “protein”, “gene”, “omics”, “hypobaric-hypoxia”, “anoxia” were used for extensive literature mining from PubMed and google search engines11, Enzyme Commission (EC) Number12, Protein family Information (Pfam) ID13, Protein Databank (PDB) ID14, The Integrative Protein Signature Database (InterPro) Id15, Single Nucleotide Polymorphism Database (dbSNP) Id16 was collected to help in cross-linking with other databases. For each protein, its GO-functional enrichment and pathway annotation were performed by Database for Annotation, Visualisation and Integrated Discovery (DAVID), and KEGG mapper tool respectively18.For each protein additional details like protein official symbol, protein aliases, chromosomal location, length, Uniprot ID19. The stringency for the search was kept at the highest level (0.9) and the filter was placed to allow a maximum of 50 associated proteins as the direct interactor of queried protein. STRING database constructs the protein–protein interactions network based on seven sources of information i.e. neighbourhood on the chromosome, gene fusion, phylogenetic co-occurrence, homology, co-expression, experimentally determined interaction, database annotated automated textmining. The interaction file was downloaded from the STRING database and was stored in JSON format.For each protein in the database, its top-50 protein interactors were identified by the Search Tool for Retrieval of Interacting proteins (STRING) webserver20; protein-drug relationship from DGIdb 3.0 database21. All these attributes were also stored in JSON files.To make the database more informative several other attributes were also added; protein-disease associations were mined from DisGeNET23.The higher the semantic similarity score, more is the probability that two genes/proteins are likely to have a similar molecular function or be involved in a common biological process22. A low semantic similarity score shows two genes imparting different molecular functions. To identify semantic similarity, each protein in the HighAltitudeOmicsDB and its top-50 direct interacting proteins was submitted to the GOSemSim R algorithm23.GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products, and gene clusters23. The results were represented in a 51 × 51 matrix. All these matrix files were also stored in the JSON file format.Gene Ontology (GO) annotation-based semantic comparisons between genes is an innovative approach to quantitatively assess the functional similarities between them. They have been extensively used across varied bioinformatics analyses24. The IDs such as Human Entrez ID, Uniprot ID, Protein Official Symbol, EC Number, PDB_ID, InterPro ID, Pfam ID, dbSNP ID, and reference PMIDs present in all tables are hyperlinked to the corresponding databases to provide additional details. The web interface also has a ‘Contact us’ page which includes a data submission form for the submission of any new data by the user. It would be reviewed and appended to the database on a regular basis.All constructed JSON files were transferred to the MongoDB database collection and uploaded to the server localhost using pymongo. Server query commands were made in the MongoDB compass. Vis.js library specifically was used to display the protein–protein interactions networkHighAltitudeOmicsDB is a user-friendly, free-to-access resource that requires no prior registration. It is a comprehensive, non-redundant, manually curated resource of genes/proteins whose expression levels are experimentally validated to be associated with high-altitude stress. The database may be surveyed using “Browse” and “Search” options.The “Browse” option allows the user to choose easily single or multiple genes/proteins from the database from a pull-down menu. Alternatively, the user may upload a file containing the protein official symbols or type the protein-official symbols. Clicking the adjacent ‘Browse’ button connects to a tabular format that hyperlinks the individual protein page. If the userlist contains protein symbols that are not in the database, a separate table highlighting the same is also provided Fig. .Figure 1The “Search”option of the database offers multiple options to explore the database based on user research interests. Search by chromosome allows to click on any human chromosome number and identify the proteins of HighAltitudeOmicsDB which lie on the respective chromosome. Search by ‘duration of experiment’ allows identifying the list of genes/proteins whose expression changes in hours/days/weeks/months/years. Searching by ‘Tissue of expression’ opens a pull-down menu from which the user can choose the tissue of interest Fig. . SearchiAdditionally ‘Associated as Biomarker’ option leads to a tabular list of proteins that have been proposed/validated as molecular biomarkers for HA-stress. The protein symbols are hyperlinked to the respective protein page which provides a link to PubMed which validates the protein as a biomarker. Additionally to fetch proteins that are DE in an altitude-dependent manner, a user-interactive slider (ranging from 2200 to 9800 mt) is provided. The user may set the slider values and fetch genes/proteins which are associated with a defined altitude range. This has been combined with (AND/OR) options with the time of exposure to HA and level of regulation (Up/Down). The user may thus be able to make combination queries like up/down-regulated proteins expressed in days at an altitude range of 2200–4500 mt. The list of these proteins can be downloaded in Excel /PDF format for further analysis.The webserver also allows to explore the proteins of HighAltitudeOmicsDB associated with a particular Transcription Factor (TF), miRNA, disease, drug, GO or KEGG pathway Fig. .Figure 3(i)Knowledge baseThe details of the protein and its association with HA are provided in the detailed information page which may be divided into six sections Fig. .(i)Knowl(ii)Interactions and semanticsThis is the first section of the database that gives general information about the protein like Protein Official Symbol, Aliases, Chromosomal location, Length, Uniprot ID, EC number, Pfam ID, PDB ID, InterPro ID, dbSNP ID which allows cross-linking to additional databases easily and quickly. The Uniprot ID is hyperlinked to the Uniprot Database.The top-50 direct protein interactors of each protein are identified from the STRING database using cut-offs described in the methodology section. The network is displayed in a user-interactive format with translation, zoom-in, and zoom-out features. The nodes are color-coded . The edges are also color-coded . The network may easily be downloaded in .sif format which can be easily visualised in network visualisation software like Cytoscape, Bina, etc. The list of interactions between them and their combined score is readily provided in a tabular format which can be downloaded in Excel/PDF format. The table is also provided with a ‘search’ option to easily search the protein of interest.(iii)Association with high altitudeThe pairwise GO semantic similarity score was calculated between the protein being studied and its top-50 interacting proteins as described in the methodology section. The results are visualised as a 51 × 51 matrix. The GO semanticsimilarity score > 0.8 is highlighted in red colour in the matrix. If any protein among the top-50 interactors is also a part of HighAltitudeOmicsDB, the protein symbol in the matrix is hyperlinked to the respective detailed protein information page within the database.This helps to identify any functional hubs of proteins that would be associated with HA stress and hence could shed light on the molecular basis for acclimatization/adaptation.(iv)Association with TFs and miRNAsFor each protein, its association with HA stress is compiled in a tabular format. The details are presented as the human protein symbol, source organism (organism in which the study was performed), tissue of expression, level of hypoxia, altitude, duration of the experiment, level of expression, fold change, experiment details, geographical location, ethnicity, control group expression, control group details and reference paper.The control group is defined based on the study plan, e.g. some studies had lowlanders as control groups and differentially expressed proteins were identified in HA-natives or lowlanders-who-ascent-to-HA. In some other studies, HA-natives were considered as controls and differentially expressed proteins were identified in lowlanders. So the control group varies as per the study and have been clearly mentioned in the database.The association of the protein as a biomarker is also compiled i.e., if the protein is ever been experimentally validated to be a biomarker, the entry in the column will be “Yes” otherwise “No”. A hyperlink to the respective publication which proves this association is also readily provided. The expression changes of a protein in different durations, tissues, and altitude conditions can be easily explored, compared, and analysed in this format.Transcription Factors and miRNAs are the two most important transcriptional and post-transcriptional regulatory molecules fine-tuning the expression of genes. Thus the list of TF and miRNAs that are known the regulate the protein being studied is presented in a tabular format. The TF association table lists the TF symbol (hyperlinked to Genecards Database), its Entrez ID, symbol and Entrez id of the protein being studied, type of association, link to publication which ascertained this association and the database from which the association is extracted. The tables are downloadable in Excel/CSV format and provided with a ‘search’ option to explore the table with a user-defined keyword Fig. .Figure 5(v)Gene Ontology and KEGG pathway annotationsSimilarly, the miRNA-gene association table lists the miRNA miRTarBase ID, miRNA, symbol and Entrez ID of the protein being studied, experiment (luciferase reporter assay/western blot/PCR/Immunohistochemistry etc.), support type and link to respective publication (hyperlinked to PubMed) which ascertained this association. The tables may be downloaded in Excel/CSV format. The table is also provided with a ‘search’ option to explore the table with a user-defined keyword.25. The KEGG pathway annotations are also compiled and presented as KEGG ID and KEGG Term. The KEGG ID is hyperlinked to the KEGG database that provides additional details about the respective pathways.The Gene Ontology annotations are presented in a tabular format. The GO ID, GO Term, and GO Type are listed. The GO ID is also hyperlinked to QuickGO which provides detailed GO annotations(vi)Association of proteins with other diseases and drugsBoth these tables can be downloaded in Excel/CSV format and have an in-built ‘search’ option for keyword search.This section provides details of drug and disease association. The information is represented in the form of tables belonging to each category respectively Fig. . The firHighAltitudeOmicsDB contains ~ 1300 associations of 820 proteins that have been found differentially expressed at high altitudes. A detailed review of the database shows that all proteins were sourced from experimental studies in 25 tissues Fig. a. These 26. At high altitude, induction of hypobaric hypoxia activates HIF protein that further regulates genes responsible for mediating changes in cellular metabolism/energetics leading to weight loss due to increase in energy expenditure27. The second biological process ‘Outer Dynein Arm Assembly’ is the process for axonemal assemblies. The increase in the length and density of axoneme-like cilia due to hypoxia has been associated with cell death28. Lastly, ‘Response To Reactive Oxygen Species’ is the reflection of the redox status of the cell, and disturbances in redox status due to hypobaric hypoxia can lead to oxidative stress and DNA damage3. Similarly, terms like ‘Fructose-Bisphosphate Aldolase Activity’, ‘Oxidoreductase Activity’, ‘Acting On Paired Donors’, ‘Incorporation Or Reduction Of Molecular Oxygen’, ‘Oxidoreductase Activity’, ‘Acting On Peroxide As Acceptor’, ‘Electron Transfer Activity’ and ‘ATP Binding’, etc. are found to be top molecular functions of proteins present in the database , ‘Outer Dynein Arm Assembly’ , ‘Response To Reactive Oxygen Species’  as the top biological processes , High Altitude Cerebral Edema (HACE), Cardiovascular Disorders (CVD), hypothermia, muscle atrophy and different types of metabolic disorders that could be life-threatening. To identify diagnostic, prognostic, or therapeutic targets, the various biomolecules that are regulated in HA stress must be studied holistically. Towards this goal, HighAltitudeOmicsDB provides a comprehensive ready-reference resource of gene/ protein expression studies associated with high altitude conditions. It contains information of about ~ 1300 protein associations for metanalysis catering not only high altitude associated maladies but similar extreme conditions like cold stress. The search helps user to filter the data based on both unique as well as combination of features. The user can choose features that are directly associated with high altitude or indirectly. The combination search helps to extract specific dataset and help in reducing stochasticity of data. All these features enhance the chances of comprehensive systematic review and metanalysis. The web-resource is not only a HA specific protein repository; it can perform unique analysis that can help in comparing and analysing genomic/transcriptomic/proteomic data. The database provides PPI network interactions of each protein with its top-50 interacting partners. These PPI networks have been found useful in illuminating the functional mechanisms for abnormities in HA conditionsHighAltitudeOmicsDB is an interactive resource and a server platform that captures and organises knowledge for genes/proteins associated with HA stress. It provides a comprehensive view of different HA-related studies; offers the annotations and visualisation of PPI networks and semantic similarities associated with gene/protein in the database. HighAltitudeOmicsDB is the first repository of comprehensive, manually curated resource of differentially expressed HA genes/proteins that were fetched using text mining and extensive literature survey. The information enables the user to browse biomolecules based on different query filters in the database, i.e., level of expression; duration of the experiment; altitude and source organism. HighAltitudeOmicsDB also encompasses protein-associated information such as TF and miRNA regulation, protein-disease association, protein-drug association. Hence the information base of HighAltitudeOmicsDB is very large and facilitates the use of this information for designing directed experiments for validation. HighAltitudeOmicsDB also identifies PPIs for each protein in the database and calculates GO semantic similarity between them.This unique feature helps to identify specific hubs of interacting proteins related to HA stress.The analysis of PPI networks and similarities would enable the user to infer mechanistic insights during HA stress. The webserver also offers functional correlation of proteins. The functional correlation includes both GO enrichment and KEGG pathway enrichment. The protein associated data can be downloaded from the database in excel/PDF format for further analysis.