PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting.

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

Fit for Purpose Approach To Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics.

Amino acid substitutions (AASs) alter proteins from their genome-expected sequences. Accumulation of substitutions in proteins underlies numerous diseases and antibiotic mechanisms. Accurate global detection of AASs and their frequencies is crucial for understanding these mechanisms. Shotgun proteomics provides an untargeted method for measuring AASs but introduces biases when extrapolating from the genome to identify AASs. To characterize these biases, we created a "ground-truth" approach using the similarities betweenEscherichia coli and Salmonella typhimurium to model the complexity of AAS detection. Shotgun proteomics on mixed lysates generated libraries representing ∼100,000 peptide-spectra and 4161 peptide sequences with a single AAS and defined stoichiometry. Identifying S. typhimurium peptide-spectra with only the E. coli genome resulted in 64.1% correctly identified library peptides. Specific AASs exhibit variable identification efficiencies. There was no inherent bias from the stoichiometry of the substitutions. Short peptides and AASs localized near peptide termini had poor identification efficiency. We identify a new class of "scissor substitutions" that gain or lose protease cleavage sites. Scissor substitutions also had poor identification efficiency. This ground-truth AAS library reveals various sources of bias, which will guide the application of shotgun proteomics to validate AAS hypotheses.

Evolutionary genetics of pulmonary anatomical adaptations in deep-diving cetaceans.

BACKGROUND: Cetaceans, having experienced prolonged adaptation to aquatic environments, have undergone evolutionary changes in their respiratory systems. This process of evolution has resulted in the emergence of distinctive phenotypic traits, notably the abundance of elastic fibers and thickened alveolar walls in their lungs, which may facilitate alveolar collapse during diving. This structure helps selective exchange of oxygen and carbon dioxide, while minimizing nitrogen exchange, thereby reducing the risk of DCS. Nevertheless, the scientific inquiry into the mechanisms through which these unique phenotypic characteristics govern the diving behavior of marine mammals, including cetaceans, remains unresolved. RESULTS: This study entails an evolutionary analysis of 42 genes associated with pulmonary fibrosis across 45 mammalian species. Twenty-one genes in cetaceans exhibited accelerated evolution, featuring specific amino acid substitutions in 14 of them. Primarily linked to the development of the respiratory system and lung morphological construction, these genes play a crucial role. Moreover, among marine mammals, we identified eight genes undergoing positive selection, and the evolutionary rates of three genes significantly correlated with diving depth. Specifically, the SFTPC gene exhibited convergent amino acid substitutions. Through in vitro cellular experiments, we illustrated that convergent amino acid site mutations in SFTPC contribute positively to pulmonary fibrosis in marine mammals, and the presence of this phenotype can induce deep alveolar collapse during diving, thereby reducing the risk of DCS during diving. CONCLUSIONS: The study unveils pivotal genetic signals in cetaceans and other marine mammals, arising through evolution. These genetic signals may influence lung characteristics in marine mammals and have been linked to a reduced risk of developing DCS. Moreover, the research serves as a valuable reference for delving deeper into human diving physiology.

The phosphorylation of the movement protein TGBp1 regulates the accumulation of the Bamboo mosaic virus.

Phosphorylation and dephosphorylation of viral movement proteins plays a crucial role in regulating virus movement. Our study focused on investigating the movement protein TGBp1 of Bamboo mosaic virus (BaMV), which is a single-stranded positive-sense RNA virus. Specifically, we examined four potential phosphorylation sites (S15, S18, T58, and S247) within the TGBp1 protein. To study the impact of phosphorylation, we introduced amino acid substitutions at the selected sites. Alanine substitutions were used to prevent phosphorylation, while aspartate substitutions were employed to mimic phosphorylation. Our findings suggest that mimicking phosphorylation at S15, S18 and T58 of TGBp1 might be linked to silencing suppressor activities. The phosphorylated form at these sites exhibits a loss of silencing suppressor activity, leading to reduced viral accumulation in the inoculated leaves. Furthermore, mimicking phosphorylation at residues S15 and S18 could diminish viral accumulation at the single-cell level, while doing so at residue T58 could influence virus movement. However, mimicking phosphorylation at residue S247 does not appear to be relevant to both functions of TGBp1. Overall, our study provides insights into the functional significance of specific phosphorylation sites in BaMV TGBp1, illuminating the regulatory mechanisms involved in virus movement and silencing suppression.

Estimating amino acid substitution models from genome datasets: a simulation study on the performance of estimated models.

Estimating parameters of amino acid substitution models is a crucial task in bioinformatics. The maximum likelihood (ML) approach has been proposed to estimate amino acid substitution models from large datasets. The quality of newly estimated models is normally assessed by comparing with the existing models in building ML trees. Two important questions remained are the correlation of the estimated models with the true models and the required size of the training datasets to estimate reliable models. In this article, we performed a simulation study to answer these two questions based on simulated data. We simulated genome datasets with different numbers of genes/alignments based on predefined models (called true models) and predefined trees (called true trees). The simulated datasets were used to estimate amino acid substitution model using the ML estimation methods. Our experiments showed that models estimated by the ML methods from simulated datasets with more than 100 genes have high correlations with the true models. The estimated models performed well in building ML trees in comparison with the true models. The results suggest that amino acid substitution models estimated by the ML methods from large genome datasets are a reliable tool for analyzing amino acid sequences.

Bioinformatics insights into CENP-T and CENP-W protein-protein interaction disruptive amino acid substitution in the CENP-T-W complex.

Kinetochores are multi-protein assemblies present at the centromere of the human chromosome and play a crucial role in cellular mitosis. The CENP-T and CENP-W chains form a heterodimer, which is an integral part of the inner kinetochore, interacting with the linker DNA on one side and the outer kinetochore on the other. Additionally, the CENP-T-W dimer interacts with other regulatory proteins involved in forming inner kinetochores. The specific roles of different amino acids in the CENP-W at the protein-protein interaction (PPI) interface during the CENP-T-W dimer formation remain incompletely understood. Since cell division goes awry in diseases like cancer, this CENP-T-W partnership is a potential target for new drugs that could restore healthy cell division. We employed molecular docking, binding free energy calculations, and molecular dynamics (MD) simulations to investigate the disruptive effects of amino acids substitutions in the CENP-W chain on CENP-T-W dimer formation. By conducting a molecular docking study and analysing hydrogen bonding interactions, we identified key residues in CENP-W (ASN-46, ARG-53, LEU-83, SER-86, ARG-87, and GLY-88) for further investigation. Through site-directed mutagenesis and subsequent binding free energy calculations, we refined the selection of mutant. We chose four mutants (N46K, R53K, L83K, and R87E) of CENP-W to assess their comparative potential in forming CENP-T-W dimer. Our analysis from 250 ns long revealed that the substitution of LEU83 and ARG53 residues in CENP-W with the LYS significantly disrupts the formation of CENP-T-W dimer. In conclusion, LEU83 and ARG53 play a critical role in CENP-T and CENP-W dimerization which is ultimately required for cellular mitosis. Our findings not only deepen our understanding of cell division but also hint at exciting drug-target possibilities.

PHACT: Phylogeny-Aware Computing of Tolerance for Missense Mutations.

Evolutionary conservation is a fundamental resource for predicting the substitutability of amino acids and the loss of function in proteins. The use of multiple sequence alignment alone-without considering the evolutionary relationships among sequences-results in the redundant counting of evolutionarily related alteration events, as if they were independent. Here, we propose a new method, PHACT, that predicts the pathogenicity of missense mutations directly from the phylogenetic tree of proteins. PHACT travels through the nodes of the phylogenetic tree and evaluates the deleteriousness of a substitution based on the probability differences of ancestral amino acids between neighboring nodes in the tree. Moreover, PHACT assigns weights to each node in the tree based on their distance to the query organism. For each potential amino acid substitution, the algorithm generates a score that is used to calculate the effect of substitution on protein function. To analyze the predictive performance of PHACT, we performed various experiments over the subsets of two datasets that include 3,023 proteins and 61,662 variants in total. The experiments demonstrated that our method outperformed the widely used pathogenicity prediction tools (i.e., SIFT and PolyPhen-2) and achieved a better predictive performance than other conventional statistical approaches presented in dbNSFP. The PHACT source code is available at https://github.com/CompGenomeLab/PHACT.

Molecular Bulkiness of a Single Amino Acid in the F1 α-Subunit Determines the Robustness of Cyanobacterial ATP Synthase.

Cyanobacteria are promising photosynthetic organisms owing to their ease of genetic manipulation. Among them, Synechococcus elongatus UTEX 2973 exhibits faster growth, higher biomass production efficiency and more robust stress tolerance compared with S. elongatus PCC 7942. This is due to specific genetic differences, including four single-nucleotide polymorphisms (SNPs) in three genes. One of these SNPs alters an amino acid at position 252 of the FoF1 ATP synthase α-subunit from Tyr to Cys (αY252C) in S. elongatus 7942. This change has been shown to significantly affect growth rate and stress tolerance, specifically in S. elongatus. Furthermore, experimental substitutions with several other amino acids have been shown to alter the ATP synthesis rate in the cell. In the present study, we introduced identical amino acid substitutions into Synechocystis sp. PCC 6803 at position 252 to elucidate the amino acid's significance and generality across cyanobacteria. We investigated the resulting impact on growth, intracellular enzyme complex levels, intracellular ATP levels and enzyme activity. The results showed that the αY252C substitution decreased growth rate and high-light tolerance. This indicates that a specific bulkiness of this amino acid's side chain is important for maintaining cell growth. Additionally, a remarkable decrease in the membrane-bound enzyme complex level was observed. However, the αY252C substitution did not affect enzyme activity or intracellular ATP levels. Although the mechanism of growth suppression remains unknown, the amino acid at position 252 is expected to play an important role in enzyme complex formation.

A single amino acid substitution alters the vanillylamine synthesis activity of Capsicum pAMT.

Some Capsicum synthesize a unique pungent alkaloid called capsaicin in their fruits. In the synthetic pathway of capsaicin, vanillylamine is produced from vanillin in a reaction catalyzed by a putative aminotransferase (pAMT). Therefore, the capsaicinoids content in the fruits is thought to partially depend on the characteristics of pAMT. Comparing Yume-matsuri (yume), C. annuum variety, and red habanero (RH), C. chinense variety, the vanillylamine synthesis activity of the placental extract was higher in yume than in RH. When each recombinant pAMT (rpAMT) was generated using the Escherichia coli expression system and their activities were compared, yume rpAMT synthesized 14-fold more vanillylamine than RH rpAMT. The amino acid sequence of yume and RH pAMT deduced from the cDNAs revealed that only 7 of 459 residues differed. When a single amino acid residue-substituted rpAMT was generated in which the 56th amino acid was swapped with one other, the amount of vanillylamine synthesis of yume and RH rpAMTs was inverted. Furthermore, it was suggested that the 56th amino acid contributed to the affinity for the coenzyme pyridoxal phosphate. Differences in the vanillylamine synthesis activity of pAMT may also lead to differences in the amount of capsaicin synthesis that accumulates in the fruit. Since capsaicin is a compound with commercial value, this finding may provide new insights into the creation of a variety that can synthesize more capsaicin.

Estimating amino acid substitution models for metazoan evolutionary studies.

Amino acid substitution models represent the substitution rates among amino acids during the evolution of protein sequences. The models are a prerequisite for maximum likelihood or Bayesian methods to analyse the phylogenetic relationships among species based on their protein sequences. Estimating amino acid substitution models requires large protein datasets and intensive computation. In this paper, we presented the estimation of both time-reversible model (Q.met) and time non-reversible model (NQ.met) for multicellular animals (Metazoa). Analyses showed that the Q.met and NQ.met models were significantly better than existing models in analysing metazoan protein sequences. Moreover, the time non-reversible model NQ.met enables us to reconstruct the rooted phylogenetic tree for Metazoa. We recommend researchers to employ the Q.met and NQ.met models in analysing metazoan protein sequences.

Site-specific glycosylation and single amino acid substitution dramatically reduced the immunogenicity of ß-lactoglobulin.

To reduce the immunogenicity of ß-lactoglobulin (BLG), we prepared recombinant BLG which has both site-specific glycosylation and single amino acid substitution (D28N/P126A), and expressed it in the methylotrophic yeast Pichia pastoris by fusion of the cDNA to the sequence coding for the α-factor signal peptide from Saccharomyces cerevisiae. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis indicated that the D28N/P126A was conjugated with a ∼4 kDa high-mannose chain. D28N/P126A retained ∼61% of the retinol-binding activity of BLG. Structural analyses by circular dichroism (CD) spectra, intrinsic fluorescence, and Enzyme-linked immunosorbent assay (ELISA) with monoclonal antibodies indicated that the surface structure of BLG was slightly changed by using protein engineering techniques, but D28N/P126A was covered by high-mannose chains and substituted amino acid without substantial disruption of native conformation. Antibody responses to the D28N/P126A considerably reduced in C57BL/6 mice. We conclude that inducing both site-specific glycosylation and single amino acid substitution simultaneously is an effective method to reduce the immunogenicity of BLG.

A high-quality genome of the dobsonfly Neoneuromus ignobilis reveals molecular convergences in aquatic insects.

Neoneuromus ignobilis is an archaic holometabolous aquatic predatory insect. However, a lack of genomic resources hinders the use of whole genome sequencing to explore their genetic basis and molecular mechanisms for adaptive evolution. Here, we provided a high-contiguity, chromosome-level genome assembly of N. ignobilis using high coverage Nanopore and PacBio reads with the Hi-C technique. The final assembly is 480.67 MB in size, containing 12 telomere-ended pseudochromosomes with only 17 gaps. We compared 42 hexapod species genomes including six independent lineages comprising 11 aquatic insects, and found convergent expansions of long wavelength-sensitive and blue-sensitive opsins, thermal stress response TRP channels, and sulfotransferases in aquatic insects, which may be related to their aquatic adaptation. We also detected strong nonrandom signals of convergent amino acid substitutions in aquatic insects. Collectively, our comparative genomic analysis revealed the evidence of molecular convergences in aquatic insects during both gene family evolution and convergent amino acid substitutions.

PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate.

Most proteins fold into characteristic three-dimensional structures. The rate of folding and unfolding varies widely and can be affected by variations in proteins. We developed a novel machine-learning-based method for the prediction of the folding rate effects of amino acid substitutions in two-state folding proteins. We collected a data set of experimentally defined folding rates for variants and used them to train a gradient boosting algorithm starting with 1161 features. Two predictors were designed. The three-class classifier had, in blind tests, specificity and sensitivity ranging from 0.324 to 0.419 and from 0.256 to 0.451, respectively. The other tool was a regression predictor that showed a Pearson correlation coefficient of 0.525. The error measures, mean absolute error and mean squared error, were 0.581 and 0.603, respectively. One of the previously presented tools could be used for comparison with the blind test data set, our method called PON-Fold showed superior performance on all used measures. The applicability of the tool was tested by predicting all possible substitutions in a protein domain. Predictions for different conformations of proteins, open and closed forms of a protein kinase, and apo and holo forms of an enzyme indicated that the choice of the structure had a large impact on the outcome. PON-Fold is freely available.

Proteome-Wide Structural Computations Provide Insights into Empirical Amino Acid Substitution Matrices.

The relative contribution of mutation and selection to the amino acid substitution rates observed in empirical matrices is unclear. Herein, we present a neutral continuous fitness-stability model, inspired by the Arrhenius law (qij=aije-ΔΔGij). The model postulates that the rate of amino acid substitution (i→j) is determined by the product of a pre-exponential factor, which is influenced by the genetic code structure, and an exponential term reflecting the relative fitness of the amino acid substitutions. To assess the validity of our model, we computed changes in stability of 14,094 proteins, for which 137,073,638 in silico mutants were analyzed. These site-specific data were summarized into a 20 square matrix, whose entries, ΔΔGij, were obtained after averaging through all the sites in all the proteins. We found a significant positive correlation between these energy values and the disease-causing potential of each substitution, suggesting that the exponential term accurately summarizes the fitness effect. A remarkable observation was that amino acids that were highly destabilizing when acting as the source, tended to have little effect when acting as the destination, and vice versa (source → destination). The Arrhenius model accurately reproduced the pattern of substitution rates collected in the empirical matrices, suggesting a relevant role for the genetic code structure and a tuning role for purifying selection exerted via protein stability.

Optimized serum stability and specificity of an αvß6 integrin-binding peptide for tumor targeting.

The integrin αvß6 is an antigen expressed at low levels in healthy tissue but upregulated during tumorigenesis, which makes it a promising target for cancer imaging and therapy. A20FMDV2 is a 20-mer peptide derived from the foot-and-mouth disease virus that exhibits nanomolar and selective affinity for αvß6 versus other integrins. Despite this selectivity, A20FMDV2 has had limited success in imaging and treating αvß6+ tumors in vivo because of its poor serum stability. Here, we explore the cyclization and modification of the A20FMDV2 peptide to improve its serum stability without sacrificing its affinity and specificity for αvß6. Using cysteine amino acid substitutions and cyclization by perfluoroarylation with decafluorobiphenyl, we synthesized six cyclized A20FMDV2 variants and discovered that two retained binding to αvß6 with modestly improved serum stability. Further d-amino acid substitutions and C-terminal sequence optimization outside the cyclized region greatly prolonged peptide serum stability without reducing binding affinity. While the cyclized A20FMDV2 variants exhibited increased nonspecific integrin binding compared with the original peptide, additional modifications with the non-natural amino acids citrulline, hydroxyproline, and d-alanine were found to restore binding specificity, with some modifications leading to greater αvß6 integrin selectivity than the original A20FMDV2 peptide. The peptide modifications detailed herein greatly improve the potential of utilizing A20FMDV2 to target αvß6 in vivo, expanding opportunities for cancer targeting and therapy.

PPVED: A machine learning tool for predicting the effect of single amino acid substitution on protein function in plants.

Single amino acid substitution (SAAS) produces the most common variant of protein function change under physiological conditions. As the number of SAAS events in plants has increased exponentially, an effective prediction tool is required to help identify and distinguish functional SAASs from the whole genome as either potentially causal traits or as variants. Here, we constructed a plant SAAS database that stores 12 865 SAASs in 6172 proteins and developed a tool called Plant Protein Variation Effect Detector (PPVED) that predicts the effect of SAASs on protein function in plants. PPVED achieved an 87% predictive accuracy when applied to plant SAASs, an accuracy that was much higher than those from six human database software: SIFT, PROVEAN, PANTHER-PSEP, PhD-SNP, PolyPhen-2, and MutPred2. The predictive effect of six SAASs from three proteins in Arabidopsis and maize was validated with wet lab experiments, of which five substitution sites were accurately predicted. PPVED could facilitate the identification and characterization of genetic variants that explain observed phenotype variations in plants, contributing to solutions for challenges in functional genomics and systems biology. PPVED can be accessed under a CC-BY (4.0) license via http://www.ppved.org.cn.

The PentUnFOLD algorithm as a tool to distinguish the dark and the light sides of the structural instability of proteins.

Intrinsically disordered proteins are frequently involved in important regulatory processes in the cell thanks to their ability to bind several different targets performing sometimes even opposite functions. The PentUnFOLD algorithm is a physicochemical method that is based on new propensity scales for disordered, nonstable and stable elements of secondary structure and on the counting of stabilizing and destabilizing intraprotein contacts. Unlike other methods, it works with a PDB file, and it can determine not only those fragments of alpha helices, beta strands, and random coils that can turn into disordered state (the "dark" side of the disorder), but also nonstable regions of alpha helices and beta strands which are able to turn into random coils (the "light" side), and vice versa (H ↔ C, E ↔ C). The scales have been obtained from structural data on disordered regions from the middle parts of amino acid sequences only, and not on their expectedly disordered N- and C-termini. Among other tendencies we have found that regions of both alpha helices and beta strands that can turn into the disordered state are relatively enriched in residues of Ala, Met, Asp, and Lys, while regions of both alpha helices and beta strands that can turn into random coil are relatively enriched in hydrophilic residues, and Cys, Pro, and Gly. Moreover, PentUnFOLD has the option to determine the effect of secondary structure transitions on the stability of a given region of a protein. The PentUnFOLD algorithm is freely available at http://3.17.12.213/pent-un-fold and http://chemres.bsmu.by/PentUnFOLD.htm .

Rational Design of Plant Hairpin-like Peptide EcAMP1: Structural-Functional Correlations to Reveal Antibacterial and Antifungal Activity.

Plant antimicrobial peptides from the α-hairpinins family (hairpin-like peptides) are known to possess a wide range of biological activities. However, less is known about the structural determinants of their antimicrobial activity. Here, we suggest that spatial structure as well as surface charge and hydrophobicity level contribute to the antimicrobial properties of α-hairpinin EcAMP1 from barnyard grass (Echinochloa cruss-galli) seeds. To examine the role of the peptide spatial structure, two truncated forms of EcAMP1 restricted by inner and outer cysteine pairs were synthesized. It was shown that both truncated forms of EcAMP1 lost their antibacterial activity. In addition, their antifungal activity became weaker. To review the contribution of surface charge and hydrophobicity, another two peptides were designed. One of them carried single amino acid substitution from tryptophan to alanine residue at the 20th position. The second one represented a truncated form of the native EcAMP1 lacking six C-terminal residues. But the α-helix was kept intact. It was shown that the antifungal activity of both modified peptides weakened. Thereby we can conclude that the secondary structural integrity, hydrophobic properties, and surface charge all play roles in the antimicrobial properties of α-hairpinins. In addition, the antibacterial activity of cereal α-hairpinins against Gram-positive bacteria was described for the first time. This study expands on the knowledge of structure-function interactions in antimicrobial α-hairpinins.

Evolutionary models of amino acid substitutions based on the tertiary structure of their neighborhoods.

Intra-protein residual vicinities depend on the involved amino acids. Energetically favorable vicinities (or interactions) have been preserved during evolution, while unfavorable vicinities have been eliminated. We describe, statistically, the interactions between amino acids using resolved protein structures. Based on the frequency of amino acid interactions, we have devised an amino acid substitution model that implements the following idea: amino acids that have similar neighbors in the protein tertiary structure can replace each other, while substitution is more difficult between amino acids that prefer different spatial neighbors. Using known tertiary structures for α-helical membrane (HM) proteins, we build evolutionary substitution matrices. We constructed maximum likelihood phylogenies using our amino acid substitution matrices and compared them to widely-used methods. Our results suggest that amino acid substitutions are associated with the spatial neighborhoods of amino acid residuals, providing, therefore, insights into the amino acid substitution process.

New amino acid substitution matrix brings sequence alignments into agreement with structure matches.

Protein sequence matching presently fails to identify many structures that are highly similar, even when they are known to have the same function. The high packing densities in globular proteins lead to interdependent substitutions, which have not previously been considered for amino acid similarities. At present, sequence matching compares sequences based only upon the similarities of single amino acids, ignoring the fact that in densely packed protein, there are additional conservative substitutions representing exchanges between two interacting amino acids, such as a small-large pair changing to a large-small pair substitutions that are not individually so conservative. Here we show that including information for such pairs of substitutions yields improved sequence matches, and that these yield significant gains in the agreements between sequence alignments and structure matches of the same protein pair. The result shows sequence segments matched where structure segments are aligned. There are gains for all 2002 collected cases where the sequence alignments that were not previously congruent with the structure matches. Our results also demonstrate a significant gain in detecting homology for "twilight zone" protein sequences. The amino acid substitution metrics derived have many other potential applications, for annotations, protein design, mutagenesis design, and empirical potential derivation.