Genetics: Early Online, published on July 27, 2017 as 10.1534/genetics.117.300064

Analysis of large-scale mutagenesis data to assess the impact of single substitutions

Vanessa E. Gray1, Ronald J. Hause1, Douglas M. Fowler1,2

1Department of Genome Sciences, University of Washington, Seattle, WA, USA

2Department of Bioengineering, University of Washington, Seattle, WA, USA

Correspondence to [email protected]

1

Copyright 2017. Abstract

Mutagenesis is a widely-used method for identifying positions that are important for function or ligand binding. Advances in high-throughput DNA sequencing and mutagenesis techniques have enabled measurement of the effects of nearly all possible amino acid substitutions in many . The resulting large-scale mutagenesis data sets offer a unique opportunity to draw general conclusions about the effects of different amino acid substitutions.

Thus, we analyzed 34,373 mutations in fourteen proteins whose effects were measured using large-scale mutagenesis approaches. was the most tolerated substitution while was the least tolerated. We found that several substitutions, including and , best recapitulated the effects of other substitutions, even when the identity of the wild type amino acid was considered. The effects of histidine and asparagine substitutions also correlated best with the effects of other substitutions in different structural contexts.

Furthermore, highly disruptive substitutions like aspartic and had the most discriminatory power for detecting ligand interface positions. Our work highlights the utility of large-scale mutagenesis data, and our conclusions can help guide future single substitution mutational scans.

2 Main text

Introduction

Making and studying mutants is a fundamental way to learn about proteins, revealing functionally important positions, validating specific hypotheses about catalytic mechanism and yielding insights into protein folding and stability. Single amino acid scanning mutagenesis, in which every position in a protein is sequentially mutated to one particular amino acid, was a key advance. By searching sequence space systematically, scanning mutagenesis enabled the unbiased identification of positions and amino acid side chains important for protein function.

The first application of scanning mutagenesis used substitutions to identify positions in human growth hormone important for receptor binding (Cunningham and Wells 1989). Alanine was chosen because it represents a deletion of the side chain at the β-carbon. In addition to alanine, many other amino acids including (Nanevicz et al. 1995), (Kanaya et al. 1990), (Valbuena et al. 2003), methionine (Woods et al. 1996),

(Borngräber et al. 1999), proline (Vandemeulebroucke et al. 2008) and (Zhang et al.

2007) have been used for scanning mutagenesis, often with a specific hypothesis in mind (e.g. that bulky amino acids are important). Nevertheless, some have suggested that alanine substitutions are maximally representative of the effects of other substitutions or that they are especially useful for identifying functionally important positions (Bromberg and Rost 2008).

Which amino acid best represents the effect of other substitutions? Which substitutions are ideal for finding functionally important positions, such as those that participate in binding interfaces?

Answering these questions is important because single amino acid scanning mutagenesis continues to be used to understand and engineer proteins. Despite the large investment in

3 scanning mutagenesis, little work has been done to systematically compare the effects of different substitutions. Some scanning mutagenesis studies compare two different types of scans (e.g. alanine and cysteine), but generally find that the information revealed by each substitution is distinct (Borngräber et al. 1999; Xiao et al. 2009). Computational predictions for all substitutions at 1,073 positions across 48 proteins in the Alanine Scanning Energetics

Database suggested that alanine substitutions correlated best with the mean effect of every mutation at each position (Bromberg and Rost 2008). However, concrete answers to these questions require comparing the empirical effects of different substitutions in many proteins.

Thus, we analyzed large-scale experimental mutagenesis data sets comprising 34,373 mutations in fourteen proteins. We found that proline is the most disruptive substitution and methionine is the most tolerated. Global and position-centric analyses revealed that histidine and asparagine substitutions best represent the effects of other substitutions, even when wild type amino acid identity or structural context is taken into account. We evaluated the utility of each amino acid substitution for determining whether a position is in a ligand-binding interface, and found that highly disruptive substitutions like , glutamic acid, asparagine and performed best. Thus, our results suggest that histidine and asparagine are the most representative substitutions, while aspartic acid and glutamic acid are the best choices for finding ligand-binding interfaces.

Materials and Methods

Data curation and rescaling

We curated a subset of the published deep mutational scanning data sets. We excluded deep mutational scans of non-natural proteins, because the mutational properties of natural and non- natural proteins could differ. The result was a set of sixteen deep mutational scans of fourteen

4 proteins (Table 1). BRCA1 and UBI4 each have two large-scale mutagenesis data sets corresponding independent experiments in which different functions were assayed (e.g. ligand binding or catalytic activity). We treated these data sets separately, and did not perform any averaging of mutational effects between the data sets. Additionally, we removed any variants with more than one amino acid substitution from all the data sets.

Most of the data sets reported mutational effect scores as the log-transformed ratio of mutant frequency before and after selection, divided by wild type frequency before and after selection.

For data sets that used a different scoring scheme, we recalculated mutational effect scores as the log-transformed ratio of mutant frequency before and after selection, divided by wild type frequency before and after selection. Given that the assays used to detect mutational effect differ, we rescaled the reported mutational effect scores for each data set. First, we subtracted the median effect of synonymous mutations from each reported effect score and then divided by the negative of the bottom 1% of reported effect scores. Finally, we added 1. In cases where synonymous mutational effect scores were unavailable, we omitted the synonymous score median subtraction step. Our rescaling scheme is expressed as

�!,!"#$!%"& – � !"#$%& !"#$#"%$&! �!,!"#$%& = + 1 −�!"#$%& !"##"$ % where S is the mutational effect score. Our normalization scheme resulted in scaled mutational effect scores where the most disruptive mutations have effect scores ≈ 0 and wild-type-like mutations have scores ≈ 1. Unless otherwise stated, we used all of the rescaled mutational effect data for each analysis. In each analysis, we used median as a summary statistic rather than mean because the frequency distributions of mutational effect are bimodal rather than

Gaussian (Fig. S1).

5 Variant annotation

DSSP was used to annotate the secondary structure and absolute solvent accessibility of each wild type amino acid in our data set (http://swift.cmbi.ru.nl/gv/dssp/DSSP_3.html). To estimate the relative solvent accessibility of amino acids, we divided absolute solvent accessibility as determined using DSSP by the total surface area of each amino acid. Amino acids with relative solvent accessibilities greater than 0.2 were labeled as “surface”, whereas amino acids with relative solvent accessibilities less than 0.2 were labeled as “buried” (Chen and Zhou 2005).

Identification of interface positions

Four proteins in our data set had high-resolution PDB structures with or nucleotide ligands, Gal4 (3COQ), BRCA1 RING domain (1JM7), PSD95 pdz3 domain (1BE9) and hYAP65

WW domain (1JMQ). We determined interface positions from the literature (Marmorstein and

Carey 1992; Doyle et al. 1996; Fowler et al. 2010; Starita et al. 2015). The interface positions in hYAP65 WW domain were 188, 190, 197 and 199. The interface positions in BRCA1 RING domain were 11, 14, 18, 93 and 96. PSD95 pdz3 domain positions were 318, 322-327, 329,

339, 372 and 379. Gal4 interface positions were 9, 15, 17, 18, 20, 21, 43, 46 and 51.

Construction of receiver-operator characteristic (ROC) curves

We constructed empirical ROC curves to illustrate the power of each substitution to discriminate between interface and non-interface positions, determined as described above. First, we defined a discrimination threshold, such that positions with a mutational effect score below the threshold were classified “interface” and positions with a mutational effect score above the threshold were classified as “non-interface.” For each substitution, we varied this discrimination threshold from the maximum mutational effect score to the minimum mutational effect score in 200 steps, calculating the true positive interface detection rate (TPR) and false positive interface detection

6 rate (FPR) at each step. The TPR was calculated by dividing the number of interface positions with scores below the mutational effect threshold by the total number of interface positions. The

FPR was calculated by dividing the number of non-interface positions with scores below the mutational effect threshold by the total number of non-interface positions. ROC curves were constructed by plotting the TPR and FPR for each of the 200 mutational effect thresholds. The area under each ROC curve was determined in R using the auc() function in the pROC package

(https://cran.r-project.org/web/packages/pROC/pROC.pdf).

Data and code availability

The data sets used in this study came from a variety of published works (see Table 1). The curated data sets and code for generating figures can be found at: https://github.com/FowlerLab/

Results

Deep mutational scanning is a method that enables measurement of large numbers of mutational effect in a protein simultaneously (Fowler et al. 2010; Fowler and Fields 2014). Deep mutational scanning can be used to quantify the effects of all mutations at each position in a protein, and is therefore a conceptual extension of single amino acid scanning mutagenesis.

The application of deep mutational scanning has resulted in an explosion of protein mutagenesis data (Fowler and Fields 2014). These large-scale mutagenesis data sets create the opportunity to assess relationships between the effects of different amino acid substitutions comprehensively.

We curated sixteen large-scale mutagenesis data sets from published deep mutational scans of fourteen proteins (Fig. 1A, Table 1). Here, we included two distinct data sets for the BRCA1

7 RING domain and for UBI4 because mutations in these proteins were independently assayed for different protein functions (e.g. BRCA1 BARD1 binding and E3 ligase activity). Our collection of data sets is ideal for an unbiased analysis of the general effects of mutations because the mutagenized proteins are highly diverse, encompassing enzymes, structural proteins and chaperones from organisms ranging from bacteria to humans. The frequency of amino acids in the wild type sequences of the fourteen proteins was similar to amino acid frequencies in all known proteins (Magrane and UniProt Consortium 2011) (Fig. 1B). For example,

(frequency = 11%) and alanine (8%) were the most frequently occurring wild type amino acids in the fourteen proteins, while tryptophan (<1%) was the rarest. However, the unbiased and massively parallel nature of deep mutational scanning experiments yielded a relatively uniform distribution of amino acid substitutions (Fig. 1C). Furthermore, the data sets were generated by different labs at different times using different types of assays, reducing the chances of bias arising from specific experimental or analytical practices. Importantly, the assay formats used for the deep mutational scans included many commonly employed in single amino acid scanning like phage display and yeast two-hybrid. Collectively, these large-scale mutagenesis data sets comprised 34,373 nonsynonymous mutations at 2,236 positions in the fourteen proteins. The data sets contained effect scores for most mutations at each position. To facilitate comparisons between each data set, we rescaled mutational effect scores for each protein, using synonymous mutations to define wild type-like activity and the bottom 1% of mutations to define lack of activity (Fig. S1A). Thus, each mutational effect score reflects the impact of the mutation, relative to wild type, with a score of zero meaning no activity and a score of one meaning wild type-like activity.

To validate the large-scale mutagenesis data, we examined expected patterns of mutational effect. For example, mutations to proline should generally disrupt protein function, as proline

8 restricts the conformation of the polypeptide backbone and eliminates the amide hydrogen necessary for hydrogen bonding. Indeed, proline substitutions were overwhelmingly more disruptive than other substitutions to protein function (Fig. 1D; Fig.S1B). In fact, proline was the most disruptive substitution in eleven of fourteen proteins and second most disruptive in the remaining three proteins (Fig. 1E). Additionally, as expected from the Dayhoff (Dayhoff 1978),

Blosum (Henikoff and Henikoff 1992) and Grantham (Grantham 1974) substitution matrices, tryptophan tended to be deleterious. Methionine was the best-tolerated substitution and therefore may be useful for identifying the most immutable protein positions. Interestingly, mutations to alanine, which is commonly employed in scanning mutagenesis, were better tolerated than many other substitutions. Other substitutions were also well-tolerated, with seven different amino acids appearing as the most tolerated across the fourteen proteins (Fig. 1D, E).

Tolerance to substitutions depends on structural context, so the variability in the best-tolerated substitution might be due to diversity in the structural composition of each protein in our data set. Thus, the large-scale mutagenesis data sets we collected generally recapitulated our expectations about the effects of mutations, despite coming from fourteen distinct proteins that were each assayed independently.

Next, we determined which amino acid substitution best represented the effects of all other substitutions. To avoid bias arising from missing data, we restricted this analysis to the 882 positions in the fourteen proteins with measured effects for all nineteen possible substitutions.

We calculated the median mutational effect at each of these 882 positions. Overall, the median effects across these positions were mildly disruptive, with a mean of 0.82 (stop ~ 0, wild-type ~

1). We found that the effects of phenylalanine, glycine, histidine, , leucine, asparagine, glutamine and substitutions were all indistinguishable from the median effects (Fig. 2A,

Table S1). However, proline, aspartic acid and tryptophan substitutions were much more

9 disruptive than the median substitution. Alanine, cysteine, methionine, , and were considerably less disruptive than the median substitution. These well-tolerated amino acid substitutions might be useful for detecting the most mutationally sensitive positions in a protein, but they are not especially representative of the effects of other substitutions.

We also examined the dispersion of each amino acid’s mutational effect about the median at all

882 positions, reasoning that representative substitutions would have minimal dispersion. Of substitutions whose effects were indistinguishable from the median effect, histidine and asparagine have the smallest dispersion (standard deviation = 0.15 and 0.14, respectively; Fig.

2B), while tyrosine (0.18), glutamine (0.16), phenylalanine (0.19), glycine (0.17), leucine (0.17) and isoleucine (0.19) all had larger dispersions. Thus, of all possible substitutions, histidine and asparagine tended to have effects closest to the median effect at the 882 positions we examined.

Next, we investigated the influence of the wild type amino acid on the effect of each substitution at all 882 positions. Wild type amino acid frequencies differed, so the number of mutations for each wild type amino acid also varied. For example, we observed 1,786 mutations at 94 positions where leucine was the wild type amino acid and 114 mutations at six tryptophan positions. We found that tryptophan positions were the most sensitive to mutation (median effect

= 0.48 at Trp positions), while glutamine positions were the least sensitive (median effect = 0.99 at Gln positions). For each substitution and wild type amino acid pair, we subtracted the median effect of all substitutions at positions with the wild type amino acid (Fig. S2A) from the median effect of the substitution at those positions. A difference greater than zero denoted a substitution that was more tolerated than the median substitution for that wild type amino acid, while a difference less than zero denoted a more disruptive substitution.

10

Hierarchical clustering of wild type amino acids based on these differences revealed two major classes (Fig. S2B). The first class included large hydrophobic amino acids, which were more sensitive to substitutions, while the second class included charged and polar amino acids, which were less sensitive to substitutions (Fig. S2A). We found that some substitutions, including histidine and asparagine, had effects close to the median substitution for most wild type amino acids (Fig. S2C). However, histidine substitutions were less disruptive than the median substitution when the wild type amino acid was tryptophan or tyrosine, and more disruptive than the median substitution when the wild type amino acid was methionine or cysteine. Meanwhile, asparagine substitutions were less disruptive when the wild type amino acid was histidine and more disruptive when the wild type was methionine. Other substitutions had more variable effects across different wild type amino acids. Thus, histidine and asparagine best represent the median mutational effect across most wild type amino acids.

Because of the comprehensive nature of the large-scale mutagenesis data sets, we could ask how well the mutational effect scores of each substitution correlated with the scores of every other substitution at each position. Thus, we calculated Pearson correlation coefficients for the mutational effect scores of each substitution pair across all positions (Fig. 2C, Fig. S3). The effects of histidine and asparagine substitutions correlated best with the effects of all other substitutions, while the effect of proline substitutions correlated worst. To visualize the relationships between each pair of substitutions, we constructed a force-directed graph (Fig.

2D). As expected, substitutions cluster by physicochemical type in the graph, meaning that similar substitutions have similar effects. Proline is not represented because its effects are poorly correlated with other substitutions. Histidine and asparagine are connected to many other

11 amino acids, owing to the high correlation of the effects of these substitutions with many other substitutions.

We next asked whether the secondary structural context of a position altered the effect of each substitution. We excluded DBR1 and GB1 from this analysis because they did not have structures of sufficiently close homologs. We used DSSP to identify 1,007 positions in the remaining proteins that were in an α-helix, a β-sheet or a turn (Kabsch and Sander 1983).

Overall, substitutions in turns were less disruptive than substitutions in α-helices or β-sheets

(Fig. 3A). However, the relative effects of each substitution in the three structural contexts were mostly consistent, especially between α-helices and β-sheets (Fig. 3B, S4A). Surprisingly, the tolerance for each amino acid substitution in the different secondary structural contexts was not strongly correlated with the frequency of that amino acid’s occurrence in known structures

(Costantini et al. 2006). For example, alanine occurs more frequently in α-helices, relative to β

-sheets. However, in our large-scale mutagenesis data sets, alanine substitutions were mildly disruptive in both structural contexts. These observations suggest that secondary structure does not dominate mutational tolerance, at least for the proteins we examined.

We next investigated which substitutions were the most representative regardless of structural context. We found that histidine substitutions have close to the median effect in α-helices and turns, but were more disruptive than the median effect in β-sheets (Fig. 3B). Asparagine and glutamine substitutions had near median effects in all three contexts. As above, we examined how well the effects of each substitution correlated with every other substitution at each position in each context. We found that the effects of histidine, asparagine and glutamine substitutions correlated best with the effects of other substitutions (Fig. S4B, C). Thus, the effects of histidine,

12 asparagine and glutamine are relatively consistent in the different structural contexts we examined, highlighting the representativeness of these substitutions.

An important use of single amino acid scanning is to identify positions in protein-ligand interfaces. In order to determine which substitution is ideal for that purpose, we analyzed the effects of substitutions in four proteins with ligand-bound structures: the hYAP65 WW domain, the PSD95 pdz3 domain, the BRCA1 RING domain and GAL4. Amongst these four proteins there were 4,884 mutations at 282 positions. We used relative solvent exposure to classify each position as either buried or on the surface. We also determined interface positions based on published structures and functional studies (see Methods). We found that substitutions at interface positions were substantially more disruptive than substitutions at buried, non-interface or surface, non-interface positions (Fig. 4A). This result was expected, given that all four deep mutational scans were conducted using selections that depended on ligand binding. Alanine, along with phenylalanine, isoleucine and methionine, are the least disruptive amino acid substitutions at interface positions, suggesting that they may not be ideal for interface detection.

We reasoned that the ideal substitution for detecting protein-ligand interfaces would exhibit a large difference in mutational effect between interface and non-interface positions. To formalize this idea, we used a mutational effect threshold. If a substitution at a particular position had a mutational effect below the threshold, we classified that position as “interface.” Conversely, if the mutational effect was above the threshold that position was classified as “non-interface.” For each substitution, we varied the mutational effect threshold from the maximum mutational effect score to the minimum effect in 200 steps. At each step, we compared the true interface positions to those determined using the mutational effect threshold procedure. We then constructed receiver operating characteristic (ROC) curves. The area under each ROC curve

13 revealed the ability of that substitution to discriminate between true interface and non-interface positions. We found that isoleucine, and alanine had the worst discriminatory power (Fig.

4B, Fig. S5). Substitutions that were highly disruptive at interfaces, like asparagine, glutamine, aspartic acid and glutamic acid, had the best discriminatory power. Next, we calculated the fraction of true interface positions detected by each amino acid substitution at a 5% false positive rate. Here, we found that asparagine and glutamine substitutions revealed over 60% of the true interface positions; aspartic acid and glutamic acid substitutions also performed well

(Fig. S6). However, alanine substitutions detected fewer than 20% of the true interface positions at a 5% false positive rate. Thus, asparagine, glutamine, aspartic acid or glutamic acid substitutions are all good choices for detecting protein-ligand interfaces.

Discussion

Single amino acid scanning mutagenesis is a widely-used method for identifying protein positions that are important for function or ligand binding. Alanine is often employed, and was selected on rational grounds as it constitutes a deletion of the side chain at the β-carbon. By analyzing tens of thousands of mutations in fourteen proteins, we have determined that alanine is not necessarily the most revealing substitution. For example, histidine and asparagine substitutions have an effect close to the median, and these substitutions correlate best with the effects of all other substitutions. Thus, they best represent the effects of mutations generally.

Asparagine, glutamine, aspartic acid and glutamic acid substitutions are highly disruptive at ligand interfaces and are consequently the most useful substitutions for detecting ligand interface positions.

However, our conclusions are based on only fourteen proteins. While these proteins are diverse in structure and function, they may not fully reflect the mutational propensities of other proteins.

14 For example, tryptophan scanning mutagenesis is often applied to transmembrane domains

(Sharp et al. 1995; Depriest et al. 2011; Rasmussen et al. 2015), which were absent from the proteins we analyzed. Thus, our conclusions are most applicable to soluble proteins.

Furthermore, we do not address specialized applications of single amino acid scanning mutagenesis. For example, cysteine scanning mutagenesis has been used to introduce disulfide bridges (Kanaya et al. 1990) and glycine scanning mutagenesis has been used to increase conformational flexibility (Weinglass et al. 2001). Our conclusions do not apply to these situations. Finally, the deep mutational scanning data we analyzed arises from genetic selections for protein function. Biochemical assays might reveal different patterns. However, we note that a few of the large-scale mutagenesis data sets we used were benchmarked against and found to be consistent with biochemical assay results (McLaughlin et al. 2012; Olson et al.

2014).

Deep mutational scanning can reveal the functional consequences of all possible single amino acid substitutions in a protein. However, these experiments can be expensive or unwieldy.

Therefore, scanning mutagenesis with one or a few amino acids will remain useful for determining functionally important positions, probing protein-ligand interactions and answering other specific questions. Our results can be used to guide future single amino acid scanning mutagenesis experiments, enabling selection of the amino acid best suited for the goals of the experiment.

Acknowledgements

This work was supported by the National Institute of General Medical Sciences

(1R01GM109110 to D.M.F.). V.E.G. is a National Science Foundation Graduate Research

15 Fellow (DGE-1256082) and R.J.H. is a Damon Runyon Cancer Research Foundation Fellow

(DRG-2224-15). We thank Lea Starita for helpful discussions and advice.

Author Contributions

D.M.F. conceived of the project. V.E.G. and R.J.H. curated and rescaled the data sets. V.E.G. and D.M.F. analyzed the data and wrote the paper.

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19 Number of Mutagenized Mutational Data set Organism Selected Phenotype Citation mutations positions completeness* (Melnikov Aminoglycosid 4,234 264 84% K. pneumoniae Antibiotic resistance et al. e kinase 2014) BRCA1 RING (Starita domain - 1,748 102 90% H. sapiens Binding activity (Y2H) et al. BARD1 binding 2015) BRCA1 RING (Starita domain - E3 4,872 303 85% H. sapiens Ubiquitin ligase activity et al. ligase activity 2015) (Findlay DBR1 144 25 30% H. sapiens RNA enzyme activity et al. 2014) (Kitzman Transcription factor Gal4 1,196 64 98% H. sapiens et al. activity 2015) (Sarkisya GFP 1,084 235 24% A. victoria Fluorescence n et al. 2016) (Mishra Hsp82 4,021 219 97% S. cerevisiae Chaperone activity et al. 2016) (Fowler hYAP65 WW 363 33 58% H. sapiens Ligand binding et al. domain 2010) (Brenan MAPK1/ERK2 4,470 359 66% H. sapiens Kinase activity et al. 2016) (Melame Pab1 1,188 75 83% S. cerevisiae mRNA binding d et al. 2013) Protein G GB1 Streptococcus (Olson et 1,045 55 100% IgG-Fc binding domain sp. group G al. 2014) (McLaug PSD95 pdz3 1,577 83 100% H. sapiens Ligand binding hlin et al. domain 2012) (Firnberg TEM1 β- 5,198 287 95% E. coli Antibiotic resistance et al. lactamase 2014) (Starita Ube4b U-box 899 102 46% S. cerevisiae Ubiquitin ligase activity et al. 2013) (Roscoe Ubi4 - activity 1,249 75 88% S. cerevisiae Ubiquitin ligase activity et al. 2013) (Roscoe Ubi4 – and activation by 1,085 60 95% S. cerevisiae Ubiquitin ligase activity Bolon E1 2014) *proportion of all possible single amino acid mutations in mutagenized region observed

20 Table 1. Large-scale mutagenesis data sets used in this study

21 Figure Legends

A B L C W W A N A 0.12 C E S 0.06 L

Y 0.08 G Y 0.04 M

H 0.04 D I 0.02 G

M 0 R E 0.00 D

F T V P

N I H K Q S C F P V R Q

K T

UBI4

DBR1

KKA2

GAL4 TEM-1 HSP82 E T

Disruptive I

PAB1 (RRM) PAB1 A

YAP65 (WW) YAP65

MAPK1/ERK2

PSD95 (Pdz3) PSD95 Ube4b (U-box) Ube4b

BRCA1 (RING) (RING) BRCA1 L Protein G (GB1) G Protein S D Q 1 M

Tolerated N H 0.9 F C 0.8 V Y G 0.7 K

effect score E 0.6 R Median mutational D 0.5 W P

M V A S T C L I Q N H G F Y R K E D W P

UBI4

DBR1

KKA2

GAL4

HSP82

PAB1 (RRM) PAB1

YAP65 (WW) YAP65

MAPK1/ERK2

PSD95 (Pdz3) PSD95

Ube4b (U-box) Ube4b

BRCA1 (RING) (RING) BRCA1 Protein G (GB1) G Protein

Figure 1. Large-scale mutagenesis data from fourteen proteins. (A) The number of single amino acid mutations with effect scores in each of the fourteen proteins is shown. (B) A radar plot shows the relative frequency of occurrence of each amino acid in the wild type sequences of the fourteen proteins (blue) or in 554,515 proteins in the UniProt Knowledgebase(Magrane and UniProt Consortium 2011) (dashed red). (C) A radar plot shows the relative frequency of each of the twenty amino acid substitutions in the large-scale mutagenesis data sets for all fourteen proteins. (D) The median mutational effect score of each amino acid substitution is shown for 34,373 mutations at 2,236 positions in all fourteen proteins. (E) A heat map shows the median mutational effect score of each amino acid substitution for each protein separately.

22 Yellow indicates tolerated substitutions while orange indicates disruptive substitutions. Amino acids and proteins were ordered according to similarity using hierarchical clustering with the hclust function from the heatmap2 package in R.

23 A B C Y * Y Y W W W V V V T T T S S S R R R Q * Q Q P P P N * N N M M M L * L L K K K Amino acid Amino I * I I H * H H G * G G F * F F E E E D D D C C C A A A -0.10 -0.05 0.00 0.05 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Difference from median effect Standard deviation Correlation with other amino acids (Pearson's R)

D

hydrophobic polar special acidic + charge

Figure 2. Histidine and asparagine substitutions best represent the effect of other substitutions. (A) For each of the 882 positions where the mutational effects of all nineteen substitutions were measured, the difference from the median effect was calculated for each substitution at each position. The median of these differences across all positions for each substitution is shown, with the red line indicating a median difference of zero. A paired, two- sided Wilcoxon rank sum test was used to determine whether each substitution’s difference from the median effect across all positions was equal to zero (* indicates substitutions with a

Bonferroni-corrected p-value > 0.01; Table S1). (B) The standard deviation of each substitution’s differences from the median effect at the 882 positions where the mutational effects of all nineteen substitutions were measured is shown. (C) For each substitution, Pearson correlation coefficients were calculated for the mutational effects of that substitution with every

24 other substitution at each position. The distribution of correlation coefficients for each substitution is shown. (D) These pairwise mutational effect score correlations are also illustrated using a force directed graph. Each node represents an amino acid and each edge force value is the Pearson correlation coefficient for the mutational effect scores of the two amino acid substitutions connected by the edge. To reduce the density of edges, only the top 40% of

Pearson correlation coefficients were included. This cutoff removed proline from the graph.

Amino acids are colored by physicochemical type. The graph was constructed using the networkD3 package in R.

25 A 1.00 Helix 1.00 Strand 1.00 Turn

0.75 0.75 0.75

0.50 0.50 0.50 Density

0.25 0.25 0.25

0.00 0.00 0.00 1.0 0.5 0.0 0.5 1.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 B Mutational effect scores Mutational effect scores Mutational effect scores Y Y Y W W W V V V T T T S S S R R R Q Q Q P P P N N N M M M L L L K K K

Amino acid Amino I I I H H H G G G F F F E E E D D D C C C A A A 0.5 0.0 0.5 1.0 1.5 0.5 0.0 0.5 1.0 1.5 0.5 0.0 0.5 1.0 1.5 Mutational effect scores Mutational effect scores Mutational effect scores

Figure 3. Secondary structural context of mutational effects. (A) Density plots describing the distribution of mutational effect scores for each substitution are shown for three different structural contexts as determined using DSSP: α-helices (left panel, N = 8,669), β-sheets

(middle panel, N = 4,796), and turns (right panel, N = 3,329). (B) The mutational effect score distributions for each substitution inα-helices (left panel), β-sheets (middle panel), and turns

(right panel) are shown. The vertical line in each panel represents the median effect score for all substitutions in that secondary structure type.

26 A B Interface Non-inferface,buried Non-inferface,surface 0.8

0.6 1.0 0.4 AUC 0.5 0.2

Mutational effect Mutational 0.0 0.0 ACDEFGH I KLMNPQRSTVWY IKAFMWPLCYRSTVHGNQDE Amino Acid Amino Acid

Figure 4. Asparagine, glutamine, aspartic acid and glutamic acid are best for identifying positions in protein-ligand interfaces. (A) The distribution of mutational effect scores for every substitution in four proteins with ligand-bound structures (hYAP65 WW domain, PSD95 pdz3 domain, BRCA1 RING domain (BARD1 binding) and Gal4) is shown at ligand interface positions as reported in the literature, and for non-interface buried positions or non-interface surface positions. (B) A mutational effect threshold was defined such that positions with a mutational effect below the threshold were classified as “interface,” whereas positions with a mutational effect above the threshold were classified as “non-interface.” ROC curves for each amino acid were generated by varying this threshold. The area under each ROC curve is shown, illustrating the power of each substitution to discriminate between interface and non- interface positions.

27 Supplemental Figures

A

40% dms_id 40% beta-lactamase Brca1_E3 Brca1_Y2H dbr1 E1_Ubiquitin E3_ligase 30% ERK2 30% gal4 gb1 GFP hsp90 kka2_1:2 Pab1 20% PSD95pdz3 20% Ubiquitin

WW_domain Percent of mutations of Percent

10% 10%

0% 0% -10.0 -7.5 -5.0 -2.5 0.0 2.5 0.0 0.5 1.0 1.5 Raw mutational effect score Scaled mutational effect score

B

A C D E F 1.00 0.75 0.50 0.25 0.00 G H I K L 1.00 0.75 0.50 0.25 0.00 M N P Q R 1.00 Density 0.75 0.50 0.25 0.00 S T V W Y 1.00 0.75 0.50 0.25 0.00 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 Scaled mutational effect score

28 Figure S1. We curated large-scale mutagenesis data sets describing the effects of 34,373 mutations at 2,236 positions in fourteen proteins. To facilitate comparisons between each data set, we rescaled mutational effect scores for each protein by subtracting the median mutational effect score of all synonymous mutations in that protein from each nonsynonymous mutational effect score and then dividing that difference by the median of the bottom 1% of mutational effect scores (see Methods). (A) Stacked histograms of the original scores (left panel) and rescaled scores (right panel) are shown. (B) Density plots of the scaled mutational effect scores for each amino acid substitution are shown.

29 A B

0.25 0.50 0.75 1.00 0.6 0 0.4 Median Difference from median

Y W W Y V M T F S V R L

Q I acid amino type Wild P C N H M G L R K P I A H

Wild type amino acid amino type Wild E G S F Q E K D C D A N ACDEFGH IK LMNPQRSTVWY T Mutant amino acid I L F T E K P Y V S A R D H N C G Q M W

C Mutant amino acid Difference from median from Difference

Mutant amino acid

Figure S2. (A) The median effect of each substitution for every wild type amino acid is presented in a heat map where substitutions with disruptive effects are shown in yellow and wild type-like effects are shown in blue. (B) For each substitution and wild type amino acid pair, we subtracted the median effect of all substitutions at positions with the wild type amino acid from the median effect of the substitution at those positions. The differences between these medians are shown in a heat map. A difference greater than zero, shown in red, denotes a substitution that was more tolerated than the median substitution for that wild type amino acid, while a difference less than zero, shown in blue, denotes a more disruptive substitution. We used

30 hierarchical clustering (linkage method: complete) on the median differences to cluster both the wild type and mutant amino acids. C) The differences between medians, calculated as described for panel B, are shown in violin plots. Each violin plot reveals the dependence of the effect of a substitution on the identity of the wild type amino acid (labeled on the plot).

31 1 P

D 0.58 0.9 E 0.55 0.81 K 0.53 0.69 0.74 0.8 R 0.58 0.71 0.73 0.84

Q (R) Coefficient Correlation Pearson 0.48 0.66 0.75 0.76 0.7 0.7 H 0.51 0.68 0.73 0.76 0.69 0.77 N 0.57 0.76 0.77 0.74 0.69 0.76 0.79 0.6 G 0.56 0.69 0.69 0.64 0.58 0.63 0.67 0.74 S 0.49 0.65 0.68 0.67 0.6 0.71 0.71 0.77 0.81 0.5 C 0.4 0.44 0.52 0.48 0.43 0.56 0.58 0.59 0.63 0.68 A 0.42 0.52 0.59 0.57 0.49 0.63 0.66 0.69 0.74 0.75 0.73 0.4 T 0.49 0.55 0.63 0.58 0.55 0.67 0.64 0.69 0.71 0.76 0.72 0.76 W 0.49 0.56 0.61 0.6 0.59 0.6 0.7 0.61 0.58 0.53 0.53 0.56 0.56 0.3 F 0.46 0.48 0.53 0.56 0.5 0.58 0.67 0.57 0.49 0.49 0.58 0.56 0.61 0.74 Y 0.47 0.56 0.62 0.61 0.58 0.63 0.72 0.61 0.54 0.54 0.53 0.53 0.55 0.74 0.79 0.2 I 0.46 0.41 0.5 0.5 0.47 0.57 0.58 0.55 0.53 0.51 0.67 0.62 0.7 0.62 0.69 0.59 V 0.43 0.43 0.52 0.5 0.47 0.55 0.57 0.53 0.55 0.55 0.67 0.66 0.7 0.58 0.67 0.57 0.83 0.1 L 0.47 0.47 0.53 0.61 0.55 0.6 0.63 0.58 0.51 0.53 0.67 0.65 0.68 0.63 0.77 0.67 0.81 0.78 M 0.35 0.38 0.48 0.5 0.4 0.56 0.57 0.53 0.47 0.48 0.67 0.6 0.64 0.58 0.68 0.62 0.72 0.66 0.8 0

Figure S3. For each substitution, Pearson correlation coefficients were calculated for the mutational effect scores of that substitution with every other substitution at each position. A correlation plot of these Pearson coefficients is shown. Color indicates the Pearson correlation coefficient ranging from 0 (light brown) to 1 (green).

32 A 1.0 Spearman's Rho = 0.91 1.00 Spearman's Rho = 0.40 1.0 Spearman's Rho = 0.23 CVM HS H QN SA L IA N QA E TLM F ST E LT M D K G Y I C D G C R F V N Q K Y I Y R F V W G 0.95 0.8 K H W 0.9 E W R 0.90 0.6 D 0.8 0.85 P Turn: Median effect score effect Median Turn: Turn: Median effect score effect Median Turn: Strand: Median effect score effect Median Strand: 0.4 P 0.80 0.7 0.4 0.6 0.8 1.0 0.4 0.6 0.8 1.0 0.4 0.6 0.8 1.0 Helix: Median effect score Helix: Median effect score Strand: Median effect score Helix B 1 C Y F W 0.69 W 0.9 V 0.57 0.5 M T 0.64 0.45 0.69 I S 0.8 R 0.63 0.56 0.74 0.77 L Q 0.58 0.51 0.57 0.73 0.62 V 0.7 P 0.52 0.5 0.6 0.68 0.56 0.67 T N

0.54 0.47 0.65 0.6 0.62 0.63 0.75 A 0.6 M L 0.46 0.46 0.63 0.61 0.59 0.6 0.7 0.76 C K 0.25 0.41 0.28 0.26 0.38 0.23 0.34 0.32 0.3 P 0.5 Amino acid Amino I 0.41 0.58 0.38 0.41 0.35 0.41 0.62 0.7 0.6 0.44 G H 0.34 0.47 0.38 0.39 0.37 0.48 0.66 0.72 0.6 0.42 0.71 S 0.4 G F 0.52 0.56 0.58 0.48 0.52 0.51 0.64 0.6 0.46 0.28 0.53 0.6 Q E 0.62 0.7 0.54 0.45 0.54 0.5 0.6 0.63 0.48 0.43 0.61 0.65 0.75 H 0.3 D 0.59 0.63 0.57 0.53 0.53 0.52 0.63 0.6 0.52 0.44 0.66 0.66 0.74 0.79 N C 0.2 0.5 0.59 0.45 0.41 0.57 0.46 0.51 0.51 0.37 0.45 0.59 0.55 0.7 0.75 0.72 K A 0.4 0.56 0.36 0.37 0.47 0.33 0.4 0.36 0.23 0.52 0.5 0.49 0.62 0.68 0.64 0.82 R 0.00 0.25 0.50 0.75 1.00 0.1 0.4 0.57 0.29 0.35 0.47 0.34 0.47 0.39 0.37 0.5 0.58 0.52 0.61 0.67 0.67 0.67 0.64 D Correlation with other

0.38 0.62 0.4 0.37 0.42 0.44 0.52 0.47 0.43 0.45 0.62 0.61 0.67 0.71 0.73 0.71 0.69 0.74 E amino acids (Pearson's R) 0 Strand 1 M Y 0.71 I W 0.9 V 0.82 0.82 L T 0.72 0.84 0.84 V 0.8 S 0.46 0.42 0.45 0.42 G R 0.41 0.34 0.37 0.44 0.8 S Q 0.7 P 0.6 0.6 0.6 0.71 0.55 0.66 C N 0.55 0.49 0.48 0.56 0.68 0.75 0.73 A 0.6 M 0.69 0.7 0.65 0.74 0.62 0.74 0.7 0.75 T L

0.42 0.41 0.42 0.39 0.53 0.5 0.43 0.38 0.51 P 0.5 K

Amino acid Amino I 0.74 0.59 0.72 0.69 0.4 0.42 0.52 0.46 0.56 0.42 F H 0.6 0.51 0.62 0.44 0.56 0.53 0.39 0.45 0.53 0.46 0.76 W 0.4 G 0.53 0.44 0.51 0.43 0.57 0.55 0.42 0.52 0.57 0.5 0.58 0.65 K F 0.37 0.35 0.38 0.34 0.47 0.52 0.38 0.44 0.53 0.5 0.46 0.61 0.83 R 0.3 E D 0.52 0.41 0.46 0.51 0.59 0.67 0.54 0.59 0.64 0.52 0.5 0.63 0.68 0.63 E 0.2 C 0.56 0.5 0.51 0.49 0.58 0.66 0.56 0.66 0.63 0.45 0.65 0.72 0.8 0.69 0.74 H A 0.61 0.57 0.63 0.62 0.62 0.72 0.55 0.64 0.72 0.52 0.61 0.62 0.77 0.69 0.71 0.81 Q 0.1 0.00 0.25 0.50 0.75 1.00 0.45 0.39 0.47 0.47 0.68 0.65 0.44 0.52 0.59 0.6 0.49 0.63 0.71 0.73 0.77 0.73 0.72 D Correlation with other 0.57 0.38 0.48 0.49 0.69 0.75 0.54 0.68 0.7 0.51 0.57 0.62 0.71 0.63 0.68 0.79 0.77 0.77 N amino acids (Pearson's R) 0 Turn 1 I Y 0.81 V W 0.9 V 0.63 0.69 C T 0.62 0.75 0.8 M 0.8 S 0.63 0.69 0.72 0.64 W R 0.7 0.76 0.77 0.77 0.77 F Q 0.7 P 0.78 0.77 0.73 0.8 0.75 0.78 L N 0.57 0.62 0.51 0.55 0.58 0.54 0.52 P 0.6 M 0.33 0.46 0.54 0.56 0.42 0.42 0.36 0.52 G L 0.64 0.73 0.61 0.61 0.72 0.64 0.71 0.66 0.6 T 0.5 K

Amino acid Amino I 0.52 0.64 0.61 0.71 0.62 0.49 0.7 0.65 0.71 0.74 N H 0.56 0.58 0.73 0.64 0.69 0.66 0.67 0.58 0.63 0.73 0.76 H 0.4 G 0.65 0.72 0.76 0.69 0.74 0.71 0.79 0.64 0.55 0.83 0.77 0.83 A F 0.54 0.6 0.7 0.6 0.58 0.57 0.68 0.61 0.81 0.73 0.77 0.79 0.84 S 0.3 E D 0.53 0.63 0.6 0.68 0.63 0.6 0.71 0.57 0.58 0.61 0.72 0.8 0.73 0.72 K 0.2 C 0.55 0.62 0.69 0.66 0.57 0.61 0.7 0.62 0.65 0.71 0.73 0.72 0.77 0.7 0.85 R A 0.54 0.67 0.71 0.73 0.61 0.66 0.76 0.55 0.64 0.67 0.73 0.79 0.77 0.83 0.78 0.77 Q 0.1 0.00 0.25 0.50 0.75 1.00 0.54 0.73 0.64 0.51 0.53 0.57 0.63 0.58 0.74 0.65 0.74 0.72 0.73 0.82 0.79 0.75 0.8 D Correlation with other 0.55 0.6 0.62 0.6 0.46 0.57 0.68 0.53 0.67 0.67 0.75 0.71 0.77 0.76 0.78 0.7 0.8 0.88 E 0 amino acids (Pearson's R)

33 Figure S4. (A) For each amino acid substitution, the median mutational effect score was calculated. The correlation between the median mutational effects for each substitution in helices, strand and turns are shown in scatterplots, and Spearman’s Rho indicates the degree of rank correlation within each scatterplot. (B) Pearson correlation coefficients were calculated for the mutational effects of each substitution with every other substitution at every position. The

Pearson correlation coefficient plots are shown separately for α-helices (top), β-sheets

(middle), and turns (bottom). (C) Boxplots show the distribution of Pearson correlation coefficients for each amino acid type in three structural contexts.

34

I A F L 1.00 0.75 0.50 0.25 0.00 D G W C 1.00 0.75 0.50 0.25 0.00 P Y S R 1.00 0.75 0.50 TPR 0.25 0.00 K M Q T 1.00 0.75 0.50 0.25 0.00 V H N E 1.00 0.75 0.50 0.25 0.00 0.00 0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.00 FPR

Figure S5. A mutational effect threshold was defined such that positions with a mutational effect score below the threshold were classified as “interface,” whereas positions with a mutational effect score above the threshold were classified as “non-interface.” ROC curves were generated by varying this threshold for each amino acid type in the four proteins with protein or DNA ligand-bound structures (hYAP65 WW domain, PSD95 pdz3 domain, Gal4 and BRCA1 RING domain (BARD1 binding)).

35

0.6

0.4 TPR

0.2

0.0 K M F P T S A W I R C Y V H D G L E Q N Amino Acid

Figure S6. A mutational effect threshold was defined such that positions with a mutational effect score below the threshold were classified as “interface,” whereas positions with a mutational effect score above the threshold were classified as “non-interface.” A barplot shows each amino acid substitution’s true positive rate (TPR) for detecting interface positions at a fixed, 5% non- interface position false positive rate.

36

Supplemental Table

AA N P.value Bonferroni.corrected.P.value A 823 4.37E-25 8.73E-24 C 859 1.26E-11 2.52E-10 D 826 1.01E-24 2.03E-23 E 809 6.82E-12 1.36E-10 F 851 1.17E-02 2.34E-01 G 836 1.41E-01 2.82E+00 H 870 4.45E-01 8.90E+00 I 815 9.06E-01 1.81E+01 K 812 2.00E-17 4.00E-16 L 788 8.14E-04 1.63E-02 M 866 3.77E-07 7.53E-06 N 850 5.56E-01 1.11E+01 P 847 2.05E-64 4.10E-63 Q 843 6.33E-01 1.27E+01 R 832 7.96E-10 1.59E-08 S 827 3.06E-27 6.11E-26 T 828 9.99E-21 2.00E-19 V 836 1.34E-08 2.68E-07 W 876 1.22E-31 2.44E-30 Y 864 1.68E-02 3.36E-01

Table S1. A table showing sample size, p-value and Bonferroni corrected p-value for paired, two-sided Wilcoxon rank sum tests of the position median effect scores versus each amino acid substitution’s effect scores. This analysis was restricted to the 882 positions where the effects of all 19 possible substitutions were scored.

37