A major advancement has recently occurred in the ability to predict protein secondary structure from sequence using artificial neural networks. This new accessibility to high-quality predicted structures provides a big opportunity for the protein design community. It is particularly welcome for membrane protein design, where the scarcity of solved structures has been a major limitation of the field for decades. Here, we review the work done to date on the membrane protein design and set out established and emerging tools that can be used to most effectively exploit this new access to structures. Approximately one-third of all human proteins are located in membranes. However, due to the difficulty in solving membrane protein structures, they have received relatively little attention by the protein design community , and ATP-binding cassette (ABC) transporters to name a few. Helix-based transmembrane structures can be further subdivided into helical bundles and helical barrels. Bundles are relatively well characterized, while helical barrels have received much less attention . The desHowever, despite a long history, the design remains challenging. Even coiled coils, which have well-characterized packing rules and have been amenable to design in their water-soluble form for a couple of decades , are stiβ, was determined for depth-related hydrophobicity to help redesign such proteins as they have very different structural features to other membrane proteins [Membrane proteins are predominantly alpha-helical but transmembrane domains composed of beta-strands are also an important group. These are more commonly seen in bacteria, mitochondria, and chloroplast . They coproteins . Most trproteins  and can proteins ). Howeveproteins .A design using natural scaffolds has been greatly constrained by the lack of solved structures that represent a tiny fraction of known structures in nature. High-quality structure prediction now potentially allows access to the majority of known transmembrane structures. Using a combination of established and emerging computational methods, it should be possible to investigate the mechanics of these structures at the atomic level and gain a much broader understanding of the features that drive function. This knowledge, coupled with high-throughput screening, removes the major constraint to a transmembrane protein design.Membrane proteins can be grouped into four major categories in the context of function and potential design. These categories are 1) receptors, (2) transporters, (3) enzymes, and (4) proteins involved in adhesion  are the most important membrane receptors in eukaryotes, comprising approximately 35% of all drug targets . They arTransporters are involved in the movement of a wide variety of molecules across membranes. These include ions, small molecules, and macromolecules such as proteins and nucleic acids. Transport is driven by facilitated diffusion or active transport. From a design and engineering perspective, ion channels have received by far the most attention. This has been largely motivated by a desire to better understand how they function, but also to probe cell biology and physiology . SeveralMembrane enzymes catalyze important chemical processes such as methylation, drug metabolism, ATP synthesis, and hydrolysis. Understanding how enzymes catalyze reactions in membranes is still extremely limited, due in large part to the lack of structures. A systematic search of the literature yields little to no examples of the membrane enzyme design, beyond deletion of the P450 enzyme membrane anchor domain to produce a soluble/secreted version of the enzyme .Challenges with designing membrane proteins are even more profound in the case of membrane enzymes, considering the competition between cavities for enzymatic activity (active site) and tight packing for thermodynamic stability of the protein  as creatCell adhesion molecules are primarily involved in mediating the interaction between a cell and its neighbors or the extracellular matrix. These proteins are crucial for maintaining tissue structure and function. An early design of adhesion proteins involved rational design to enhance affinity between integrin lymphocyte function-associated antigen-1 (LFA-1) and its ligand: intercellular adhesion molecule (ICAM-1) . More reA number of useful tools are available to broaden and enrich our understanding of transmembrane protein mechanics from structural investigation. These will be critical for processing the vast number of new protein structures predicted by alpha-fold. Previous examples of redesigning membrane proteins with computational approaches have shown the potential for tuning interactions within membrane proteins and introducing new functionalities with established protocols . In this5-106 atoms) are achievable in the span of weeks. In this section, previous MD studies on the structure and function of select membrane proteins are reviewed.Molecular dynamics (MD) simulations of membrane proteins are generally computationally more resource intensive due to the requirement for phospholipid molecules (lipid bilayer membrane). Until very recently, multimicrosecond MD studies on such systems were only feasible using a coarse-grained approach. Currently, microsecond-long MD simulations on large protein-membrane systems . The translocation of a ligand mediated by ABC requires a large-scale conformational change in the transmembrane domains (TMDs), converting the cytoplasm open-state to the periplasm open-state and vice-versa. The free energy required for such a process is provided by the ATP binding, hydrolysis, and dissociation of the product at the nucleotide-binding domains (NBDs).ABC transporters have been the subject of several computational and computational-experimental studies , 79, 80), 8079, 82, CO2, NH3, glycerol, urea, and water were examined [Aquaporins and aquaglyceroporins are a family of integral membrane proteins that function in a tetrameric form and are responsible for the selective translocation of solutes across the cell membranes. In a comprehensive MD study, the selectivity and permeability of human AQP1 (hAQP1)  channel width and length, hydrophobicity, hydropathy, polarity of lining amino acids, lipophilicity, solubility, and water number density. Traditionally, the channels were examined by size-based approaches, which provides useful information about the permeation pathway. However, gates that are not based on steric occlusion of the pathway cannot be identified by this method. In this section, some of the available and more widely used tools for the analysis of membrane protein properties, including channel properties, will be introduced.http://www.holeprogram.org/, and the source code is freely available to download at https://github.com/osmart/hole2. The program was developed for single structure analysis and is not designed to process MD simulation trajectories, where sequential reads and processing is required. HOLE accepts PDB format and to initiate the analysis requires the user to specify an initial point along the channel as well as a vector that represents the direction of the channel. A HOLE run on a PDB structure provides pore radius of the protein, which can be illustrated in different ways, including a 2D plot of pore radius against distance along the channel and a 3D representation of the channel. HOLE is also capable of estimating the conductance of a channel based on the protein structure.Developed based on the first pore analysis study , the HOPoreWalker  is an auhttp://opm.phar.umich.edu/) or the MEMEMBED [probe radius and interior threshold, which control the alpha-shape surfaces and beta-shape cavities of the protein, respectively. MOLEonline uses an interactive graphical interface and allows the user to select a property of interest to be illustrated along the pathway. These properties include charge, hydrophobicity, polarity, lipophilicity, solubility, and mutability. MOLEonline employs three different radius definitions to characterize the width of the pore, namely, radius, free radius, and BRadius. Radius of pore is defined as a sphere within the pore limited by the three closest atoms, while free radius calculation uses the three closest backbone atoms, which allows for sidechain flexibility. To include the local flexibility of the residues, BRadius calculates the RMSF of the amino acids from the B-factors included in the structures and adds it to the calculated radius  was introduced in 2019 and is freely available. CHAP is written in C++ language and unlike HOLE is capable of MD trajectory analysis. CHAP can be run on PDB structures directly and provides radius and hydrophobicity profiles. Additional information such as water number density and free energy profiles require performing MD simulations. The resulting MD trajectories are used by CHAP through a workflow to estimate and produce such profiles. These steps are comprehensively illustrated and discussed in the original paper . In thisComputational protein design tools typically involve backbone sampling, scoring functions, sequence optimization, and functional site design . NaturalFree energy calculations can be used to accurately predict the contribution of individual amino acids to protein function and as such can be used to assist protein design. Thermodynamic integration (TI) and free energy perturbation (FEP) are currently among the most accurate computational techniques for free energy calculation. They have been consistently shown to match experimental binding free energy values with minimal error –102. DruIt is timely to emphasize that while binding affinities for protein–ligand and protein–protein systems can be experimentally determined for soluble proteins, in many cases, membrane protein systems do not possess soluble parts and lipid membranes are required for their stability and function and hence experimentally determining the binding affinity is not possible with current methods.Machine learning and particularly deep learning are leading to major breakthroughs across a broad range of sectors, particularly image classification and natural language processing. These successes have now been extended to solving major challenges in computational biology, alphafold being perhaps the most significant to date. Protein design using generative models such as generative adversarial networks (GANs) are still a very nascent field, with just a handful of experimental validated methodologies published –110. It Directed evolution is done by diversifying the native protein sequence and filtering the designs with the most promising function . DiversE. coli, and introduced with fluorescent agonists to select variants with maintained binding affinities [in vitro. Since purification and production of membrane proteins remains a challenge, methods that can be done within mammalian systems offer a great advantage. Such an example is the viral evolution of genetically actuating sequence (VEGAS) system. This uses the RNA alphavirus Sindbis, to link viral vector replication with the diversification of the target protein under selective pressures [For membrane proteins specifically, directed evolution has been used to improve GPCR stability in detergents and increased expression levels—the two limitations affecting membrane protein studies . A librafinities . These wfinities . This meressures . Their pressures . Other mressures  and CRISressures  rely on ressures . They weressures . They shressures . Althougressures .After successful diversification, libraries of up to a billion gene variants can be generated, and high-throughput screening strategies can be used to select for functional variants . FunctiFluorescence-based assays work by measuring the emission of fluorophore upon interaction with the protein target of interest. This method of detecting protein–protein interactions and ligand binding has high sensitivity, is noninvasive to the sample, and has relatively fast response times . FunctioIn addition to functional changes, designed protein stability is also important to evaluate. One method to measure stability is thermal scanning which measures protein unfolding with a fluorescent marker. To integrate this approach as a high-throughput screen, GFP-thermal shift assays were used to quantify ligand binding to solute carrier (SLC) transporters . A methoElectrophysiology methods allow for the tracking of ions through voltage-sensitive channels, and the historical gold standard is the patch-clamp method. More recently, automated electrophysiology assays have been developed that offer a promising alternative. IonWorks and its derivatives have shown success in single-cell recording along with population patches. The SyncroPatch 384PE is another device that can clamp 384 cells in parallel to record around 20,000 data points a day  Taken alTransmembrane protein design holds much potential for a broad range of advancements in synthetic biology and biotechnology. Despite decades of development however, the field is still woefully underexplored. This has almost entirely been due to the paucity of solved structures, meaning that rules of assembly and packing have been difficult to elucidate. An absence of natural scaffolds and design rules meant that large swathes of membrane protein types have received little to no attention by protein designers. With the recent advent of high-quality structure prediction in the form of alphafold, this barrier has been very significantly lowered. The field is now poised to systematically investigate and elucidate structure and function at the atomistic level. To do this, a range of established and emerging tools are at the disposal of investigators in the field. Deploying these tools to dynamically model and characterize predicted structures will yield an unprecedented number of natural scaffolds for design. It is likely that the structural coverage engendered by systematic investigation of predicted structures will provide membrane protein assembly insights that will further catalyze design efforts in this area. It will also provide invaluable training data for emerging methods for protein design using artificial neural networks. With this advancement, future challenges that remain include developing systems for directed evolution within mammalian cells that include negative selection pressures. As synthetic biology and cell-based therapies begin to gain traction with regulators, protein designers are now very well positioned to explore this new frontier of cell–cell interaction.


The study of protein folding mechanism is a challenge in molecular biology, which is of great significance for revealing the movement rules of biological macromolecules, understanding the pathogenic mechanism of folding diseases, and designing protein engineering materials. Based on the hypothesis that the conformational sampling trajectory contain the information of folding pathway, we propose a protein folding pathway prediction algorithm named Pathfinder. Firstly, Pathfinder performs large-scale sampling of the conformational space and clusters the decoys obtained in the sampling. The heterogeneous conformations obtained by clustering are named seed states. Then, a resampling algorithm that is not constrained by the local energy basin is designed to obtain the transition probabilities of seed states. Finally, protein folding pathways are inferred from the maximum transition probabilities of seed states. The proposed Pathfinder is tested on our developed test set (34 proteins). For 11 widely studied proteins, we correctly predicted their folding pathways and specifically analyzed 5 of them. For 13 proteins, we predicted their folding pathways to be further verified by biological experiments. For 6 proteins, we analyzed the reasons for the low prediction accuracy. For the other 4 proteins without biological experiment results, potential folding pathways were predicted to provide new insights into protein folding mechanism. The results reveal that structural analogs may have different folding pathways to express different biological functions, homologous proteins may contain common folding pathways, and α-helices may be more prone to early protein folding than β-strands. The study of protein folding mechanism is an important part of basic science and has vital significance in many aspects. The key to the study of protein folding mechanism is to capture the conformational changes from the fast protein folding process. Biological experiments are more difficult to obtain protein metastable structures than computational methods. It is computationally expensive to simulate the complete folding pathway of macromolecular proteins by molecular dynamics methods. Here, we design a protein folding pathway prediction method based on conformational sampling to provide new ideas for existing research. This method obtains the structural information of the intermediate state through large-scale sampling and clustering, combines the resampling algorithm to explore the transition probability of the intermediate state, and predicts the protein folding pathway. The results show that we validate the method on five widely studied proteins, and also reveal the folding mechanism of some proteins. And Pathfinder complements the existing protein folding data from the perspective of computational simulation, which needs to be further verified by biological experiments. Finally, Pathfinder predicts unresolved protein folding pathways, providing insights into unknown folding mechanisms. The splendid computational success of AlphaFold2  and RoseThere are biological experimental methods to explore protein intermediate states and folding pathways –32, suchComputational simulation of protein folding can make up for the deficiency of biological experimental methods, and is an effective way of studying protein folding pathways . MoleculConformational sampling algorithms such as Monte Carlo (MC) can be applied to folding simulations of template-free and larger proteins . The CA-In this work, we propose a protein folding pathway prediction algorithm (Pathfinder) based on conformational sampling. We obtain the structural information of the seed states through large-scale sampling and explore state transition probabilities through resampling. Pathfinder captures the information  to predict folding pathways. Pathfinder is tested on our developed dataset (34 proteins). For 11 widely studied proteins, we correctly predicted their folding pathways Fig A in  and specNcon is the number of residues whose distance between them is less than 8Å. The ΔSij is the sequence separation between residue i and j, and L is the length of the protein. On this basis, the residue contact order of i -th Rco(i) was designed to assess the local folding completion of intermediate states:Å from i -th residue. The dij is the distances between residue i and j. The contact order can evaluate the degree of folding completion through the intermediate state structure information. The residue contact order can capture the folding nucleus information and key residue information during the folding process, as described in Fig E in Related studies have shown that the logarithm of experimental protein folding rates depends on the local geometry and topology of the protein’s native state . ContactThe IgG-binding B1 domain of protein G GB1,  and IgG-I2) start to form. Then the β3 and β4 formed as shown in GI2. In addition, the β1 and β2 of GI2 is represented in helical, which is different from the native structure in 1 and β2 is formed at the N-terminus (as shown in N’). Our GB1 predictions are complete consistent with biological experiments [The predicted protein folding pathways are shown in eriments –58 and cI1) suggest that the β1 and β2 hairpin structures at the N-terminal are formed earlier than the β3 and β4 structures, which was different from the folding mechanism of GB1. After β3 and β4 folding, the loop of LB1 folds to stabilize. Similarly, C-terminal β turns  and β4 start to form in the LI1. The predicted results of LB1 are almost consistent with the biological experiments [3 and β4 and loop. This is because the fragment assembly method completes both the helix and sheet folding early in conformational sampling.As shown in 1f, the I1 of LB1 was designed . We also5 may be responsible for misfolding [The study on the folding of SRC Homology 3 Domain provides extremely valuable information for the molecular mechanism of amyloid formation and the cytotoxicity of protein aggregates, which is of great significance for better understanding the pathological process and exploring the possibility of future treatment . Extensisfolding .2, β3, and β4 forms. Then, both loops marked in I2 of 1 and β5 at the N-terminal and C-terminal of the protein to complete the folding. The predicted results are completely consistent with the biological experimental results [2, β3, and β4 can be observed, and the RT-Src loop usually only forms a rough outline. Therefore, it may be the instability of the RT-Src loop leads to insufficient constraints on β5 during SH3 folding, leading to misfolding. The result also show that these homologous proteins may share the same folding pathway.Here, we predicted the folding pathway of SH3 from Escherichia coli eSH3,  and chic results , which vThe lysin domain (LysM) is a ubiquitous and versatile peptidoglycan-binding module found in bacterial proteins . BecauseThe folding pathway predicted by Pathfinder is shown in By comparing the intermediate state data or folding pathway information collected in relevant literature with the predicted results by Pathfinder, we verified the folding pathways of 11 proteins. The above is the specific analysis of 5 proteins, and the folding pathways of the remaining 6 proteins are shown in Fig A in Because it is difficult to obtain the intermediate state, biological experiments usually study the folding mechanism by analyzing the key residues in the folding process. Pathfinder predicts protein heterogeneous conformation and folding pathways by sequence and native state. This may provide new ideas for the study of folding mechanism. The 13 proteins in Fig B in Response regulator proteins utilize distinct molecular surfaces in inactive and active conformations for various regulatory intramolecular and intermolecular protein interactions . MoleculTheir contact orders all increase exponentially, indicating that the intermediate conformational structure occurs more in the early stage of folding. In the early stage of folding, these proteins all form multiple α-helices, further illustrating that the helical structure may be formed earlier than the β-strand. During the subsequent folding pathway, internal β-strands are formed step by step. By comparing the intermediate states of these proteins, we found that several α-helices and β-strands often form a super-secondary structure or folded nucleus. It is the mutual extrusion and collapse of these folded nuclei that stabilize the protein fold. Pathfinder can predict the protein folding pathway by sequence and native to study the protein folding mechanism of the unresolved native conformation.We apply efficient sampling algorithms to explore intermediate states and develop a protein folding pathway prediction algorithm based on conformational sampling. Pathfinder captures information between intermediate states to predict protein folding pathways. The results show that Pathfinder can extract the commonality from the folding pathways of multiple proteins, and discover the folding mechanisms of some proteins. For example, structural analogs may have different folding pathways to express different biological functions and provide insights for protein design. The proteins of the SH3 family may have the same folding pathway, and the instability of the loop region leads to insufficient force on the local structure, resulting in misfolding. During folding, α-helices may form earlier than β-strands because of the influence of hydrogen bonds. Further, we explore the predictive preference of Pathfinder. We found that Pathfinder more easily predicted proteins containing both β-strand or α-helix structures as shown in result. For proteins containing only α-helix it may be biased due to the easier assembly of helices by fragment assembly Fig H in .ref2015 energy force field. However, the evaluation of the contact order showed that the degree of folding did not show a clustering trend. Therefore, the inaccuracy of the energy force field leads to the flawed prediction of the folding pathway. Since Pathfinder is based on the simulation of protein folding guided by the Rosetta force field. Energy force fields trained with deep learning may be applicable to folding pathway prediction for more proteins.As shown in Fig I in Compared with traditional biological experiments, Pathfinder obtains approximate protein intermediate state structures and, at the same time, predicts the order in which these intermediate states appear, enriching protein folding data. For molecular dynamics simulations, if the protein sequence is too long, the vast calculation parameters will inevitably limit the simulation of the folding process. The method can be combined with molecular dynamics simulations to provide new insights into methods for computationally simulating protein folding. At the same time, Pathfinder can analyze family proteins or protein collections under different classifications, and explore protein folding mechanisms from a broader perspective.Protein conformational sampling can provide new ways to explore folding pathways. In this study, we hypothesized that the protein folding information from unfolded state to folded state may be implied in the conformational sampling process in the energy landscape ,70, and ref2015 energy force field. The test set we collected includes 34 proteins. The folding data of 6 proteins are collected from the HDX experimental database of Start2Fold [Because the conformational sampling process is greatly affected by the energy force field, we predict the structures of 193 proteins through the basic Rosetta ab initio modeling algorithm and the art2Fold .19 proteart2Fold , the othClassicAbinitio protocol in Rosetta [http://old.robetta.org/). In the stage (A), a large-scale conformational sampling algorithm is used to obtain a mass of decoys. The cluster centroid obtained by clustering is selected as the seed state. In the stage (B), based on a modified energy function, conformational resampling is not constrained by local energy traps to explore transition probabilities between seed states. Finally, the protein folding pathways are inferred by transition probabilities using a dynamic programming algorithm in the stage (C).The pipeline of Pathfinder is shown in  Rosetta ,74, frag Rosetta  is used G MMC trajectories to explore energy basins with high repetition rates in folding pathways. Each MMC trajectory generates about 360,000 conformations, where accepted conformations are saved as decoys. There are at least 13000 decoys from all MMC trajectories for cluster. We cluster decoys into centroids using Spicker [S centroids. Because of redundancy among centroids, we merge centroids with TM-score>τ to generate N seed states. In particular, the lowest global energy basin is represented in the seed state 1  -th conformation. Based on the modified energy function, Ct escapes the local basin more easily than f(Ct) guiding resampling is defined as:Er(Ct) is Rosetta energy function and Ep(Ct) is an energy function which designed to modify the original energy function. T(t) is reduced as the number of samples increases to offset the large energy gap between the unfolded state and the folded state.L is the length of the protein, the μ is the initial value to avoid excessive energy at the beginning of sampling. The energy function Ep(Ct) is designed as:Ep(Ct−1) is the previously accumulated energy function to maintain enough energy to rush out of the energy basin. S is the dihedral angles difference between Ct and Ct−1, designed as:i residue of t -th conformation. The modified energy function was compared with the benchmark conformational sampling algorithm  is the maximum transition probability to aji is the transition probability from seed i to seed j. Because of the imperfection of the Rosetta force field, it is difficult to sample protein conformations to the native state. Therefore, the known native structure is used as the end point of the folding (ψ1(i) = 1), and the folding pathway I* is reversely inferred.Based on the seed states transition probability matrix S1 TextFig A. 11 validated protein folding pathways. Fig B. 13 protein folding pathways to be verified. Fig C. 6 defective protein folding pathways. Fig D. Two cases showing limitation of contact order. (a) is the contact order and intermediate state line diagram of 5l8i protein. (b) is the intermediate state structure of 5l8i protein. (c) is the line graph of contact order and intermediate state of 1opa protein. (d) is the intermediate state structure of 1opa protein. It can be found that the contact order of intermediate state 1 is relatively high because there is a cavity in the middle of the structure in the native state. In the metastable structure, the contact order of the intermediate state 1 is higher than that of the intermediate state 2, which is about to form the cavity, because the cavity has not yet been formed. Fig E. The spline connection graph of the normalized residue contact order. (a) is a distance map of the intermediate states of the GB1 protein. (b) is the residue contact order diagram of the intermediate state of GB1 protein, where orange represents the intermediate state and gray represents the native structure. (c) The ratio of the residue contact order between the intermediate state and the native structure is represented in color on the structure. The more similar the residue contact order in the intermediate state is to the natural structure, the higher the ratio, and the more it tends to blue. Comparing the residue contact order with the native structure, the folding degree of the intermediate state and the order of appearance of the secondary structure can be further analyzed. Furthermore, the residue contact order information can be represented by a three-dimensional structure to better observe the folding nucleus. Fig F. LB1 sampling process diagram. A total of 10125 accepted process points were generated during one conformational sampling process of LB1. We gave the first 3000 sampling data and analyzed the first 30 conformations. And present part of the conformation. Fig G. Predicted folding pathway of NuG2. The probability of the I1, I2 and N’ pathway is 0.67 and the probability of the I1, I3 and N’ pathway is 0.32. Fig H. Folding pathway prediction results of helical proteins. Pathfinder’s predictions for proteins containing only α-helices may be biased. The intermediate state predicted by Pathfinder may complete the assembly of the α-helix of each link in the early stage of folding, so that the sampling of the late stage is more about the sampling of the loop region. However, the experimental results shown that the helix at both ends of the 1yyj protein is folded to be stable at the late stage of folding. Fig I. Line graph of sampling times and energy. (a) is the sampling trajectory of 1e0m protein, and (b) is the sampling trajectory of 1opa. Among them, the protein energy of 1e0m has almost no drop, and 1opa is more in line with the normal conformational sampling process. Fig J. Prediction of defective protein folding pathway analysis. We analyzed the cause of insufficient folding pathways for other proteins . Using score_jd2 protocol to calculate the intermediate state of these proteins based on the ref2015 scoring function. We further use fastrelax to relax the intermediate states. The processed intermediate state also uses the score_jd2 protocol to calculate the energy item, which is shown in the fourth column of the figure. The results show that the intermediate states of 1ten and 1ubq proteins follow the sampling process of energy decline in the ref2015 energy force field, which indicates that the energy force field is biased in sampling the intermediate states of these two proteins. Fig K. Sampling Process Analysis Diagram. (a) is the energy and RMSD scatterplot of the conformations of the sampling process. Red dots are the conformations of the sampling process under the modified energy force field. Black dots are the benchmark conformations of Rosetta’s conformational sampling process. (b) is the trajectory of energy variation with the number of samples. Red is the sampling trace of the modified energy force field, and black is the sampling trace of the baseline Rosetta. Table A. The test set of Pathfinder. Table B. The parameter of Pathfinder. Table C. Performance improvement table of modified energy function (MEF). Table D. Table Running Speed of Pathfinder on dataset.(DOCX)Click here for additional data file.


Identifying the probable positions of the protein side-chains is one of the protein modelling steps that can improve the prediction of protein–ligand and protein–protein interactions. Most of the strategies predicting the side-chain conformations use predetermined dihedral angle lists, also called rotamer libraries, that are usually generated from a subset of high-quality protein structures. Although these methods are fast to apply, they tend to average out geometries instead of taking into account the surrounding atoms and molecules and ignore structures not included in the selected subset. Such simplifications can result in inaccuracies when predicting possible side-chain atom positions.We propose an approach that takes into account both of these circumstances by scanning through sterically accessible side-chain conformations and generating dihedral angle libraries specific to the target proteins. The method avoids the drawbacks of lacking conformations due to unusual or rare protein structures and successfully suggests potential rotamers with average RMSD closer to the experimentally determined side-chain atom positions than other widely used rotamer libraries.rotag and available at GitHub: https://www.github.com/agrybauskas/rotag, under GNU Lesser General Public License.The technique is implemented in open-source software package  The essential step in predicting protein interactions with different proteins or ligands is analysis of the side-chain flexibility , coarse-ructures .The general technique of building rotamer libraries starts with selecting high-quality structures from the PDB .It was noticed that the diversity of the structures is essential for building rotamer libraries. After choosing the initial set of protein crystal structures, side-chains were grouped by amino acid and, depending on the application of the library, by other criteria. Until sufficient quantity of protein structures were solved, all side-chain ψ angles . Both baRotamer libraries can also vary in the ways how the most frequent dihedral angles are sampled from the statistical population. The common strategies are either using discrete or continuous generalization of the frequently occurring dihedral angles. Binning is one of the discrete methods to achieve the selection of the most frequent angles. Other common ways of selecting the most common angles are kernel density functions  and BayePre-calculated libraries from selected sets of protein structures are very convenient for side-chain prediction applications, because there is no need to regenerate them each time during the use. On the other hand, it was noticed that some flexible side-chains can end up having inaccurate models. For example, long chains, such as Arg, Glu, Gln, Lys and Met, when exposed on the surface of the protein, can be hard to fit to the electron density maps . Even saAlphaFold2  tools such as We suggest an approach to generating rotamer libraries that does not require collecting and filtering a set of protein structures. In our method, this is achieved by scanning through the conformational space of the specified side-chains and excluding dihedral angles that produce too high potential energies. The use of dead-end elimination (DEE)  parameter file and using the grid-based cell-list neighbour finding algorithm, also called cubing procedure . First, Z-axis and then the rotation around the bond is applied. By storing pre-multiplied constant terms of the matrix equation . First of all, high-quality structures were chosen, i.e. those with resolution rotag performance against other widely used rotamer libraries, comparable libraries were generated using rotag. These rotag libraries were compared against Dunbrack BBDEP ]. The test is unhelpful by itself without the knowledge of the magnitude of two measurement differences with respect to their standard deviations. Therefore, the effect size or Cohen’s D parameter was also calculated [R package: effsize (https://CRAN.R-project.org/package=effsize)].The statistics for comparing bcRMSD were chosen such that the nonnormal distributions would be analysed. For this purpose, Wilcoxon signed-rank test was chosen [R package: The structures with bcRMSD higher than 0.1 Å were selected for further analysis. Such bcRMSD cutoff value was chosen from experience as significant for protein modelling applications. It is also supported by values obtained from Luzzati distribution  that desrotag to search for side-chain positions, ASN The calculation of bcRMSD requires that the atom positions and the atoms themselves from the selected rotamer library could be comparable to those from experimental data. Sometimes, side-chain atoms in crystallographic data are indistinguishable if the resolution of the structure is not high enough or if chemical group is symmetrical. ASP is an example of such amino acid where  residue . By reduIdeally, the best rotamer generation method should produce rotamers with atom positions as close to experimental positions as possible with the least overall choice count. By scanning all the possible side-chain conformations using user-defined dihedral angle change, the minimal bcRMSD value could be produced. However, the amount of dihedral angles to choose from would be computationally excessive in side-chain structure prediction applications if the dihedral angle range are too finely divided. Side-chains such as ASP, ASN, GLU, GLN, PHE, TRP, HIS, and TYR have dihedral angle distributions with high standard deviations near local maxima and for this reason lack the discrete identifiable rotamer angles . Moreoverotag and pre-calculated rotamer libraries are stored in PDBx/mmCIF format  with regards to experimental data. The last amino acid with discrepancy on bcDA is LEU. Interestingly, angle distribution modes of Dynameomics BBDEP are aligned with experimental values, but the difference of dihedral angle variances is noticeable  in PDB as of 26 February 2022, so the sample size of these side-chains are insufficient for building rotamer libraries. Methods that were used in building Dynameomics library could be applied, but the actual libraries were not built. Using rotag calculations are specific for the target protein structure, they are dynamic, scanning angles can be changed, adjusted, and recalculated. Also, rotag removes impossible conformations right away. During the selection of rotamers, rotag helps to address the nonrotameric conformation problem (rotag comparisons with other widely used rotamer libraries showed its capability to have more accurate results when dealing with outliers. Although for some side-chains, such as LYS and MET rotag might offer excessive rotamer choices or prolonged dihedral angle calculations for ARG, GLN, ILE, LEU, LYS, and MET (rotag could be used along the existing rotamer libraries where critical amino acids are being thoroughly studied in order not to miss important rare rotamers.The main idea of creating rotamer library that could be generated for the specific protein structure was to avoid averaging problems from pre-calculated side-chain dihedral angle datasets. Outliers are important especially when they are in conformationally restricted positions in active sites or protein–protein interfaces. Since  and MET , the excrotag is its ability to deal with nonstandard amino acids. There are only a handful of structures with selenocysteine or pyrrolysine in the PDB which precludes collection of extensive statistics. This is, however, not a problem for rotag since its method does not require pre-calculated averaged dihedral angle datasets.One of the greater advantages of the method used in In the future, bond angle bending and bond length change will be included as additional degrees of freedom in order to get even more diverse possibilities of side-chain conformations. This type of rotamer library generation method is a first step for developing side-chain positions solver independent of pre-calculated rotamer libraries that could be used in the protein–protein or protein–ligand docking.btad429_Supplementary_DataClick here for additional data file.


In recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed.Two alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly. Semiempirical methods have been shown to be successful in modeling various phenomena that occur in enzymes. For example, the complete mechanism of the hydrolysis of a peptide bond by chymotrypsin was modeled , and theBecause the focus of semiempirical method development had been on modeling much smaller chemical systems, little or no effort had been expended in modeling non-interacting moieties, and, as a result, when validation tests for experimentally-determined protein geometries were applied to calculated geometries, a large number of unrealistic close contacts were found.Experimentally, protein geometries of the type stored in the Protein Data Bank  (PDB) arExperimentally-determined protein geometries are routinely analyzed for geometric anomalies, such as “clashes,” where pairs of atoms that are not covalently bonded and do not have a non-covalent stabilization interaction, such as hydrogen bonds, are closer together than would be expected. Clashes in proteins can be identified and quantified by programs such as Molprobity , which cAlthough clashes are useful in validating experimental geometries, from a computational chemical perspective, where energies dominate, clashes are of less importance because the energies involved are relatively small. Nevertheless, it is not sufficient that a computational model should be chemically realistic: it should also be physically realistic. Addressing this issue required that a small change be made to the semiempirical model.The earliest of the modern semiempirical Self-Consistent Field (SCF) methods, MNDO , 7, was These earlier methods did not adequately address the issue of non-covalent interactions, so hydrogen bonding and dispersion interactions were, for all practical purposes, non-existent. Two changes were made in 2013 to address this deficiency. A new method, PM7 , was devAlthough both PM6 with post-SCF corrections and PM7 were able to model enzymes and other proteins, justification for the further application of these methods was brought into question by the potentially serious problem mentioned earlier. Most proteins in the PDB, especially those deposited in recent years, have a low clashscore, but a survey of geometries of representative protein systems optimized using PM6-D3H4 and PM7 had an average clashscore over four times larger. These very high clashscores cast serious doubt on the level of confidence that could be placed on the predictions of these methods. Confidence in the computational model would be increased if changes could be made to the model so that the clashscores improved.Analysis of the methods revealed that there was an absence of the weak, long-range, van der Waals (vdW) repulsive interaction. If present, such a repulsive interaction would be able to increase the interatomic separation between pairs of atoms that would not otherwise be attracted together, thereby reducing the incidence and severity of clashes.A potentially important application of semiempirical methods is the prediction of protein – ligand interaction (PLI) energies. In contrast to the small range of types of non-covalent interaction found in proteins, the range of possible non-covalent interactions between ligands and proteins is very large. As a result of individual non-covalent interactions between pairs of atoms, one on a ligand, the other on the protein, some other pairs of atoms are pulled inside their contact radii. In semiempirical methods, hydrogen bonding and electrostatic interactions that pull otherwise very weakly interacting pairs of atoms inside their contact radii are responsible for the unrealistic clashes observed in earlier methods. Interactions of this type are important in PLI energies, and only recently has access to data on such interactions become available.PM6-D3H4 was chosen as the starting point for the new method because it has been shown to be substantially more accurate  than PM7cA,B, was modified by the addition of a term proposed recently [Addressing the problem of adding a term to represent the repulsion of two otherwise non-interacting atoms in a protein requires an understanding of the environment of the atoms. Ignoring all other atoms, the force between them would be negligible until they approached the vdW contact distance, at which point a repulsive force would appear and increase as the interatomic distance decreased. In the absence of the other atoms, non-interacting atom pairs could not approach closer than the vdW contact distance, but, with current semiempirical methods, if the other atoms exerted an appropriate force on them, they could be pulled inside the vdW contact distance. An inspection of the Molprobity results showed that this fault could be corrected by the addition of a small repulsive force to the computational model for specific diatomic interactions. At vdW distances, all energy terms between pairs of atoms are very small, so any function that would produce the required repulsion force would only need to operate in that region. In PM6 the expression for the core-core repulsion energy between pairs of atoms is scaled using diatomic parameters , so, forrecently , as showa, b, and c are parameters which depend on the elements of the two atoms involved, and r is the interatomic distance. This function would be used at all interatomic distances greater than c Ångstroms; at smaller distances the additional term would be replaced simply by a.In this, Because of its form, this function would have a negligible effect on the heat of formation but would exert a weak repulsive force on atoms in the region of their vdW contact distance.Parameter optimization involves a training set of reference data. The original training sets used in developing PM6 and PM7 contained a large amount of data for systems that were not relevant to modeling biochemical systems, such as high-energy species such as difluoromethyl and the nitrogen dioxide cation. Many of these data were identified as being potentially detrimental to the accuracy of the current method, and consequently were removed from the training set.Because the size of the parameter set was increased by the parameters in the repulsion functions, extra reference data had to be added to the training set, to allow these parameters to be defined. Each of these data was designed to represent one and only one diatomic vdW repulsion. Conventional reference data from experiment or from high-level calculations were unsuitable for this task; instead, small chemical systems that were proxies for each diatomic interaction were used. Each repulsion interaction was represented by two systems: one consisting of two small molecules separated by a large distance, typically 50 Å, and the other consisting of the same two molecules separated by the vdW contact distance. The difference in energy of these systems then formed a proxy for the vdW repulsion.In practice, clashes involved only a small number of diatomic interactions and these were restricted to the elements H, C, N, O, and S. Examples of these are listed in Table The energy of the non-covalent interaction between a ligand docked to the binding site of an enzyme is a useful measure of the binding efficiency of that ligand, and, by inference, the effectiveness of the pharmacophore. One estimate of the accuracy of prediction of individual non-covalent interactions can be obtained by comparing the calculated binding energy and reference binding energies.Non-covalently bound ligands are stabilized by electrostatic, hydrogen bonding, and dispersion terms. However, in addition to these terms, there are other protein-ligand interactions that are destabilizing. This occurs when various stabilizing non-covalent interactions cause a ligand to approach a protein so closely that other pairs of ligand and protein atoms, which otherwise would not interact, start to repel each other. The absence of these destabilizing interactions has been reported  to give Correcting this deficiency was straightforward. Using CCSD(T)/CBS methods, a benchmark data set of repulsive interaction energies, named R739x5, was developed . Entries-1 above the well-separated components. The other two moieties were the individual species, either calculated individually or as a well-separated pair. The resulting computed heats of formation were then used for calculating the interaction energy.For both non-covalent and repulsive interactions each reference datum involved three species. One consisted of the complex of the two species involved; for non-covalent interactions, these were two species in their equilibrium geometry, and for the repulsive interactions, as recommended , these w-1). During the parameter optimization of PM6, reference data representing hydrogen bonds were introduced. These involved systems where non-covalent interatomic separations spanned the range from 1.5 to 2.0 Ångstroms, in contrast to the normal O-H and N-H covalent bond lengths of about 1.0 Ångstroms, and the energies involved were only a small fraction of those involved in covalent interactions. In order to compensate for the difference in magnitude, the weighting factor for hydrogen bond energies was increased to about 10/.Before individual reference data can be used in optimizing the values of parameters, they must first be rendered dimensionless. Prior to the development of PM6, only data relating to individual species were used, and the weighting factors for heats of formation spanned a small range, up to a maximum of about 1.0/ and 100/.Each type of diatomic clash was represented by precisely one Clash datum in the training set. In the original data set there would be a large number of similar diatomic pairs separated by distances approximately the same as that in the Clash datum, but in contrast with the Clash datum, these would, by definition, be connected covalently by one or more atoms. Because of the large disparity in the number of data in the original set, the weighting factor for systems in the second set had to be increased significantly. This, together with the increase needed to compensate for the very small energies involved at such large distances, made it necessary to increase the weighting factor for Clash data to between 30/ between calculated and X-ray backbone geometries was calculated; this provided a useful scalar measure of the difference between the geometries of two systems. Only atoms with the PDB label N, CA, or C in each system were used, and the geometry of the calculated system was rigidly rotated and translated as required to achieve the RMSD. Individual RMSD’s are shown in Table Given that most proteins are highly compact macromolecules  and thatProteins present a unique problem in that they are too large to be used in conventional parameter optimization operations. This operation uses reference data for a large number of small systems, so the properties for each datum can be calculated rapidly, allowing a complete parameter optimization to be carried out in only one or two days using a 3GHz computer. However, calculation of even one datum for an entire protein system would require more computational effort than that for all the other systems combined, so simply adding proteins to the optimization was impractical.An alternative, using a single proxy reference datum to represent each particular type of clash, eliminated the need for any proteins to be present in the optimization.A consequence of this use of a single proxy datum to define three new parameters was that these parameters would not be uniquely defined, an ambiguity that was resolved by the presence of the large amount of reference data on small systems. Their presence in the optimization automatically had an effect on the new parameters, allowing them to become uniquely defined.Optimizing parameters for use in modeling proteins has hitherto been impractical because of the very large computational effort that would be required. However, in the approach described here, by constructing proxy reference data that represent individual interactions of the type that occur in proteins but do not occur in small chemical systems, a considerable reduction in time has been achieved. When these proxy reference data representing long-range interactions are used together with enough reference data on small systems to allow short-range chemical properties to be properly represented, optimization of parameters for use in modeling proteins requires approximately the same time as that for a conventional optimization.A comparison was made of individual clashes in original PDB structures and their equivalent in PM6-ORG optimized structures. As expected, all the results from geometry optimization were consistent with the assumption that the largest changes would involve the most flexible coordinates. This can be illustrated using the potassium channel protein 1JMV, the protein whose experimental geometry had the largest clashscore. Within this system, the largest clash involved Ala31 and Leu35 of chain A, as shown in Figure A deficiency in two semiempirical methods, PM7 and PM6-D3H4, which had allowed pairs of atoms in proteins to approach closer than expected, has been corrected by the addition of a simple empirical function that represented the vdW repulsion, a term that had not been important when only small systems were being modeled. After this correction was made, the clashscores decreased to less than 20% of their values when PM7 and PM6-D3H4 were used, and to 60% of the original PDB clashscore.Previous semiempirical methods modeled chemical systems by using two types of interatomic interaction: those that represented strong covalent interactions, such as chemical bonds, and the much weaker non-covalent stabilization interaction caused by hydrogen bonds, electrostatics, and dispersion effects. The addition of the vdW repulsion interaction represents a new and third type of term in NDDO semiempirical methods. Although this term is the smallest and weakest of the three interactions, its absence from the computational model was the direct cause of large geometric errors in the predicted structures of proteins.Optimizing semiempirical models to more accurately reproduce properties of proteins was achieved by the development of reference data that acted as proxies for individual diatomic interactions in proteins. This strategy is highly flexible, and would likely be applicable to addressing other errors found while modeling biochemical systems.Compared with PM7, PM6-ORG represents a reduction of 8% in AUE in predicted heats of formation, a 21% reduction in AUE in dipole moments, a 10% reduction in AUE in bond-lengths, and a 12% reduction in AUE in bond-angles. One metric, the ionization potential, showed an increase of 17%.-1, on average, and would have rendered any comparison of AUE meaningless. This particular fault was corrected in the current parameterization.As noted earlier, because the D3H4 correction was added to the un-modified PM6, the heats of formation predicted by PM6-D3H4 were shifted by more than 10 kcal mol-1 for PM6-D3H4 and PM7, to about 12 kcal mol-1 for PM6-ORG. Because the range of errors was significantly reduced, the incidence of large errors in non-covalent interactions would also be reduced. This would be expected to result in a corresponding improvement in the accuracy of the prediction of protein - ligand interaction energies, particularly those involving unusual combinations of elements.The most dramatic change was in the repulsive contacts, where the range of errors decreased considerably, from about 30 kcal molAverage errors for clashscores, backbone deviation, and percent volume change are shown in Table The largest reduction in errors occurred in the clashscores, which implies that the computational model is more realistic and should help alleviate some of the doubts regarding the usefulness of these methods for modeling protein behavior. Both the RMSD and the volume change are intermediate between PM6-D3H4 and PM7, although the RMSD for PM6-ORG was significantly worse than that for PM6-D3H4. This deterioration could be attributed to the possible presence of large distortions in the X-ray structures of the sodium and potassium channel proteins, as implied by their reported unusually large PDB clashscores. When these two proteins were removed from the RMSD calculation, the PM6-OPT value dropped to 0.91 Å.2O306 formed two hydrogen bonds, both of length 2.01 Å, with the peptide oxygen of Ala111. A survey was run to determine the frequency of a water molecule forming two hydrogen bonds with the same oxygen atom. No examples were found when the PDB or PM6-ORG geometry was used, but 35 examples were found when PM6-D3H4 was used.An anomaly was found in the PM6-D3H4 distribution of hydrogen-bond lengths involving two oxygen atoms in the region between 1.7 and 2.1 Å, in that the number of hydrogen bonds predicted using PM6-D3H4 was significantly larger than that predicted using the original PDB geometry, as shown in Figure Although the objective of the new method is to improve the ability to model enzyme mechanisms and other dynamic phenomena that occur in proteins, any investigation into such phenomena would, because of their great complexity, require considerable effort. Therefore, for the purposes of this report, only static properties were examined.2O402, and in turn the H2O402 moiety forms strong hydrogen bonds with Asp85 and Asp212.Bacteriorhodopsin is a good example of a protein that contains a large amount of α-helix. Its structure consists of a stack of seven α-helices surrounding a protonated Schiff base formed by Nξ on Lys216 and a retinal group. This Schiff base forms  strong hAll these features, i.e., the helices, the Schiff base and the compact hydrogen bonded structure, were reproduced by PM6-ORG. In both the original PDB structure and in the optimized PM6-ORG structure the charge distribution in the Schiff base was delocalized over the extended conjugated π-system of the retinal. This prediction is consistent with a recent report  on relatp-hydroxybenzylidene-imidazolidone composed from residues Ser65, Tyr66, and Gly67. This chromophore is held in place by a strong electrostatic interaction between the ionized guanidine group on Arg96 and the O2 on the imidazole ring. Like the chromophore in bacteriorhodopsin, the chromophore in GFP has [Green fluorescent protein (GFP) provides a good example of the other main secondary structure in proteins: the anti-parallel β-sheet. It consists of a single protein chain folded into 11 β-strands that form a barrel. Inside the barrel is a chromophore of the type  GFP has  an exten GFP has  of the nThe secondary structures of both bacteriorhodopsin and GFP are held together by a large number of hydrogen bonds. As a result, both of these structures are relatively rigid.In contrast to these two proteins, the structure of barnase involves two α-helices and a multi-strand antiparallel β-sheet, as well as a β-hairpin bend, and several intrinsically disordered regions, and, as such, provided a useful test for the ability of PM6-ORG to reproduce the experimentally-observed geometry. Given that the PM6-ORG RMSD of the barnase backbone, 0.86 Å .The first step in converting a reference datum into a form suitable for use in optimizing parameters is to render it dimensionless. In this work, data representing energies were assigned weighting factors that depended on the energy range involved, from about 1 up to 100/(kcal molIn addition to the weighting factors depending on the types of reference data used in the training set, weighting factors can also be used in altering the focus or objective of the method. In the present work, the aim was to reduce the clashscores and to improve the accuracy of prediction of intermolecular interactions of the type found in ligands non-covalently bound to proteins. The results of using this particular focus can be seen in Table The following summary of the features of the new method can be used in deciding its applicability to any specific protein system.Overall, clashscores improved in going from an average of 32.4 for PM6-D3H4 and 28.5 for PM7 to 4.8 for PM6-ORG.The average RMS errors in the geometry of protein backbones increased by 14% relative to PM6-D3H4, but were still very slightly smaller than those in PM7. This increase was accompanied by an increase of 0.6% in the difference of the volumes of the proteins.Two proteins optimized using PM6-D3H4 had severe faults. In transcobalamin, a spurious covalent bond formed between Cys98 and H2O2031, and in zinc endoprotease a spurious bond formed between Cys112 and Arg79. Neither of these faults was present in the PM6-ORG optimized structures, nor were any new faults found.Most of the limitations in the applicability of PM6-ORG to modeling proteins are caused by the underlying software limitations of the MOPAC program. Because of their size, solving the SCF equations for proteins always requires the MOZYME procedure to be used. In its current form, MOZYME is limited to closed-shell systems, so open-shell systems of the type encountered in free radical biochemistry and photochemistry, e.g., photosynthetic pathways and other proton pumps, cannot be modeled.III as in transcobalamin, and porphyrin with FeII in P450. In their ground states the geometries of these systems can be modeled using MOZYME, as shown in 2V3N and 7TTP, but modeling electronic phenomena of the type that occur in reaction mechanisms when the oxidation state of a metal atom changes would not be possible.Another class of systems that could cause problems involves heterocycles with transition metal ions in their center, such as corrin with CoAbout 7,000 atoms is the practical upper limit of the size of system that can be modeled. The most time-consuming step in a modeling study is the initial geometry optimization. For a system of 7,000 atoms and using a 3GHz computer this would require about one to two CPU weeks. This limit can be avoided for proteins in which allosteric behavior is not important. In such systems, all chemical effects on binding sites and active sites caused by distant atoms can be ignored, so that the system could be trimmed down to only include atoms within about 12 Ångstroms of any atom of interest. The resulting system would then be in the 1,000 – 2,000 atom range and simulations would run much faster. In practice, initial geometry optimizations of trimmed systems required only one or two days and subsequent optimizations would run in just a few hours.Although the addition of the vdW repulsion term to the computational method improved the clashscores for proteins, in order to carry out meaningful simulations other criteria relating to the computational model must also be satisfied. These criteria all involve issues relating to chemical behavior. Resolving some of these might present difficulties, so the following suggestions are provided in the hope that they might prove useful.Before attempting to model protein behavior it is essential that several steps must be carried out. The first, and by far the most important of these, is that the model should be as realistic as possible.A good starting point for this is to hydrogenate the experimentally-generated geometry of a protein together with any associated small molecules such as water and, if present, a ligand. Three steps are involved.+2, Na+, K+ and Cl-. Second, ionize all ionizable residues. Third, add and delete hydrogen atoms to ensure that the starting model structure is correctly ionized.First, add hydrogen atoms to neutralize all sites in the protein except for non-covalently bound atoms that would normally be ionized, such as Cain vivo environment of a protein, the use of implicit solvation is essential. For this, the COSMO model is ideal.To simulate the Generating a self-consistent field for macromolecules is best done using the MOZYME localized molecular orbital approach. MOZYME begins by generating a Lewis structure for the entire system. One result of this process is the generation of a list of all ionized sites, information which is extremely useful when checking for errors in hydrogenation and bonding. The LMO’s are then constructed and used in solving the SCF equations. An incidental beneficial result of using Lewis structures as the starting LMO’s is that a common problem when working with proteins, ensuring that the net charge is correct, is eliminated.Experimental geometries that have low clashscores typically do not present any problems, but geometries with high clashscores can present problems with hydrogenation and with solving the SCF equations. The default method in MOPAC for hydrogenating systems relies on the topology of the system, but large clashes can alter the topology to such a degree that the resulting system becomes unrecognizable. By using keywords to selectively edit the topology, a chemically-sensible structure can be generated and hydrogenated. Because the starting LMO’s used by MOZYME depend on the topology, an incorrect topology could cause MOZYME to generate an incorrect SCF. This particular fault can also be avoided by using the same keyword that was used to edit the topology for hydrogenation to edit the topology used in constructing the starting LMO’s.An example of a non-standard topology can be seen in PDB ID: 1PY4, a protein of 388 residues. The X-ray structure of 1PY4 has a resolution of 2.90 Å and a wwPDB validation clashscore of 104, and consists of four chains, A, B, C, and D. Chain D is unusual in that the carboxylate group of Glu16 is in close proximity to the four backbone atoms of Ser20, and Cγ on Glu16 is close enough to Cγ on Lys19, 1.73 Å, to give rise to the incorrect assumption that these atoms are covalently bonded together. The orientation of these residues is shown in Figure Five topologic bonding errors were generated by MOPAC and are shown in green. By selectively deleting these connections, the correct topology would be revealed and the system could then be correctly hydrogenated. The same set of deletions that was used in hydrogenation would also be used to correct the topology in preparing the LMO’s for MOZYME. When the geometry was optimized using PM6-ORG the calculated clashscore dropped from 104.50 to 1.58. As would be expected from a system with such a high clashscore, the RMSD, 1.564 Å, was unusually large.The next step would be to optimize the geometry of the system and compare the results with the experimental geometry. Known faults in the calculated geometry, such as systematic backbone motion, can safely be ignored; instead, attention should be focused on the local environment of sites of interest. In general, the geometries of reaction and binding sites are quite strongly conserved , so any 2+ ion being mistaken for a water molecule, can also be identified by the relatively large distortions that occur in water clusters.In side-chains of the residues glutamine and asparagine, where incorrect assignment of oxygen and nitrogen can occur, and histidine, where flipping is possible, errors in conformer orientation can usually be detected by the large distortions that appear in the optimized conformer, or by obviously incorrect or missing hydrogen bonds. More exotic errors, such as a MgBy using a reference data set tailored to focus on systems relevant to protein chemistry and adding computed benchmark reference data for repulsive interactions, as well as adding a small correction to account for long-range repulsions, an improved semiempirical method for modeling proteins has been developed. Four faults in its parent method, PM6-D3H4, were corrected: the result of the addition of D3H4 to PM6 was a large increase in average errors in heats of formation; a known fault in PM6 produced spurious sulfur-oxygen and sulfur-nitrogen covalent bonds; a tendency to form too many hydrogen bonds; and, in proteins, the large number of clashes detected using Molprobity. Computed properties of 21 proteins were significantly improved, and an examination of sites of interest indicated that the new method, named PM6-ORG, should be more suitable than the preceding methods in MOPAC for modeling enzyme mechanisms and protein-ligand interactions.


Plant-based protein products, represented by “plant meat”, are gaining more and more popularity as an alternative to animal proteins. In the present review, we aimed to update the current status of research and industrial growth of plant-based protein products, including plant-based meat, plant-based eggs, plant-based dairy products, and plant-based protein emulsion foods. Moreover, the common processing technology of plant-based protein products and its principles, as well as the emerging strategies, are given equal importance. The knowledge gap between the use of plant proteins and animal proteins is also described, such as poor functional properties, insufficient texture, low protein biomass, allergens, and off-flavors, etc. Furthermore, the nutritional and health benefits of plant-based protein products are highlighted. Lately, researchers are committed to exploring novel plant protein resources and high-quality proteins with enhanced properties through the latest scientific and technological interventions, including physical, chemical, enzyme, fermentation, germination, and protein interaction technology. With the rise in consumers’ demands, animal proteins are gradually in short supply, which has led to a trend towards seeking alternatives to animal proteins. In addition, with the evolving focus on nutrition and health, consumption patterns have changed towards differentiation, individualization, and simplification. When it comes to protein consumption, people are considering a number of future-oriented social issues such as health, safety, environmental pollution, and resource availability . In termAlthough plant-based protein foods have been accepted by consumers, there is still scope for development and their future commercial market potential should not be underestimated . Among pThe objective of this updated review is to explore the latest types of plant-based protein products as an alternative to animal protein, evolving processing technologies, functional properties, and their health benefits. Based on common and popular plant-based protein products on the market, we present the types of animal protein alternatives, mainly concentrating on four categories: plant-based meat, plant-based eggs, plant-based dairy products, and plant-based protein emulsion foods. At present, there is a large gap between the use of plant proteins and animal proteins, leading to some challenges such as poor functional properties, insufficient texture, low protein biomass, allergens, and off-flavors, etc. Thus, this review provides updated information on the main methods and techniques to improve the functional properties of plant proteins and to optimize the source and quality of plant proteins. We explore the possible solutions to address the existence of obstacles and constraints for plant-based alternative proteins, which not only contribute to the development of plant-based protein but also strengthen its economical and accessible nutritional value in the food and health industries. Moreover, we also highlight the properties and nutritional and health benefits of plant-based protein products.With the increasing demand for meat, environmental requirements, and health needs, there is an alternative market base for plant-based protein meat, with the number of commercially available products increasing year by year that are widely recognized by consumers . Plant-bThe plant-based alternative proteins currently used in commercially available plant-based meat products are mainly soy, pea, and wheat proteins, followed by potato, rice, and mung bean proteins . Soy proPlant-based milk is considered an alternative to milk for personal health reasons, especially for some people with metabolic disorders and allergies, for example, lactose intolerance (LI) and milk protein allergy (CMPA) . In recePlant-based milk refers to products made from plants and/or their products that contain certain proteins as the main raw material, to which food additives and food supplements can be added. Plant-based proteins have different nutritional values, so fortifying them by adding enzymes or blending two or more plant proteins to achieve a product with the same high nutritional value as milk can be an important step in processing . The addAn increasing interest in the development of egg alternatives is driven by various factors such as consumer preference, allergen reduction, improved nutrition, etc. . RondoniFrom all these egg substitute applications, it is noted that a combination of plant proteins, hydrocolloids or emulsifiers, and other food ingredients is expected to be used in the development of egg substitutes. At present, the development of egg-mimicked products by researchers is mainly based on plant proteins and polysaccharides . CompareDue to “clean labelling” requirements, there is an increasing demand for the development of plant proteins as natural food emulsifiers that are comparable to the emulsifiers traditionally used, such as phospholipids, fatty acid glycerol monoesters and their derivatives, fatty acid esters of sucrose, and fatty acid esters of sorbitan and their derivatives . There iMayonnaise has a high proportion of internal phases; the droplets forming the emulsion have a small particle size, allowing a more viscous texture while exhibiting viscoelastic rheological properties with yield stress. At present, some of the studies on the preparation of mayonnaise-like emulsions from alternative egg yolks mostly use various types of plant proteins in combination with polysaccharides or complex polymers as emulsifiers or add polysaccharide thickeners to assist in achieving the rheological properties and stability of mayonnaise. Nikzade et al. (2012) prepared mayonnaise-like emulsions using soy milk and other stabilizers , replacing all egg yolks . With thv/v) prepared by Wong et al. (2013) showed a synergistic enhancement of gel strength in the hot gel ..65].Chenopodium quinoa and field pea increased after germination, probably due to a lower consumption of its own proteins than the production of new ones, resulting in an increase in the content of various proteins after germination [Pisum sativum L. grains, showing that storage proteins are hydrolyzed by proteases to release amino acids or small molecule peptides, which synthesize new proteins with small molecule peptides by the oxidation of the carbon backbone after deamination, resulting in an increase in soluble protein content [Germination of cereal and legume seeds causing changes in proteins, amino acids, and other components is an interesting field with great potential. Current research on the germination of pulses and cereals focuses on the nutritional quality and functional activity of the flavonoids and phenolics during seed germination, such as antioxidants and changes in protein fractions and subunits. For example, the crude protein content of mination . Concha  content . In addi content . GABA is content ,70. Thes2 fluid treatment, etc. Moderate physical modifications have little effect on the primary structure of the protein. However, it can improve the functional properties of the protein by altering the secondary and tertiary structure. Plant proteins are modified by heat deformation or by adding some edible gum to make the protein gel well and then by high-speed shearing to reduce the protein particle size to obtain plant-based protein emulsion foods [Protein modification refers to the artificial modification of protein structures by suitable methods according to certain specific needs, thus improving the processing properties of the protein . In geneon foods . The phyChemical modification is based on the principle of interaction between chemical reagents and proteins, where the peptide bonds of some proteins are broken or other functional groups are introduced into the protein structure, thereby improving its functional properties. For plant-based protein products, the hydration properties , surface properties , structural properties , and organoleptic properties  are improved . The enhEnzymatic modifications are divided into two categories: nonhydrolytic modifications and hydrolytic modifications. Among them, enzymatic hydrolysis modification refers to the process of using specific proteases to break down large molecules into small peptide chains of various chain lengths . It is aThe comparison of the effects of plant versus animal protein diets on chronic disease in humans is shown in In addition, plant proteins can be made more digestible through soaking, cooking, concentration, or separation techniques. Plant-based protein products are cholesterol-free and lactose-free, so they are not a burden to eat and at the same time meet the body’s protein requirements. In terms of the state of protein presence in food, plant proteins tend to be combined with carbohydrates (non-starch polysaccharides and dietary fiber), while some animal proteins contain relative higher fats, e.g., fatty meats, milk, egg yolk, etc. ,82,83. TIn the past years, plant protein has emerged as a sustainable and important nutritional source and the use of plant-based protein products to supplement or replace some of the deficiencies of animal protein has emerged as a hot research topic. As a high-quality protein resource, plant proteins have very good potential for exploration, which plays a more important role in meeting the demand for food proteins. At present, the commercialization of plant-based protein products as an alternative to animal protein is facing problems such as small ranges of available products and bottlenecks in processing technology. In addition, the functional properties of plant-based proteins are poorly compared with those of animal proteins, particularly in terms of solubility in water and stability and ability to foam. These plant-based proteins are reported to have good functional properties following some modifications and techniques, including physical, chemical, enzyme, fermentation, and germination. Mixed proteins or other components, fermentation, and germination have been found to be the main reconstruction techniques for creating protein complexes with nutritional and functional properties. There are some challenges to address some of the technical barriers of plant-based proteins, particularly solubility and nutritional properties. Plant proteins should be investigated more as alternatives to animal proteins, especially the combination of two or more technologies, such as protein interaction technology  and fermentation or germination technology , to produce new plant protein complexes with enhanced properties. Compared with animal proteins, plant proteins have a unique role in human health against cardiovascular disease, type 2 diabetes, and certain tumors. The only areas of concern are the presence of allergens and off-flavor components of plant protein-based foods, which affect the flavor and safety of plant protein products and limit their widespread use in the food sector. There is an urgent need to increase the direct use of plant-based proteins in the human diet and to modify the functional and organoleptic properties of proteins through various processing processes. Moreover, plant-based alternative proteins face great challenges in terms of low cost and efficient preparation, precise control of processing, nutritional and safety evaluation, and flavor and texture regulation; therefore, more emphasis is needed to accelerate their industrial application by advancing in process technology and new approaches. Future research in the field of plant proteins can be summarized in two basic strategies, namely protein fortification and complementation. Although much has been achieved in various processing technologies for plant proteins, there is a need to develop fully effective processing methods (precise and efficient) to exploit proteins with high nutritional value and functional properties and thus hasten the further industrialization of plant proteins.