key: cord-0305363-79lt5mmw
authors: Chen, N.; Das, M.; LiWang, A.; Wang, L.-P.
title: Sequence-based Prediction of Metamorphic Behavior in Proteins
date: 2020-02-28
journal: bioRxiv
DOI: 10.1101/2020.02.27.967935
sha: ef621f7ce5212076029a03530334ada4439e8bf3
doc_id: 305363
cord_uid: 79lt5mmw

An increasing number of proteins have been demonstrated in recent years to adopt multiple three-dimensional folds with different functions. These metamorphic proteins are characterized by having two or more folds with significant differences in their secondary structure, where each fold is stabilized by a distinct local environment. So far, about 90 metamorphic proteins have been identified in the Protein Databank (PDB), but we and others hypothesize that a far greater number of metamorphic proteins remain undiscovered. In this work, we introduce a computational model to predict metamorphic behavior in proteins using only knowledge of the sequence. In this model, secondary structure prediction programs are used to calculate diversity indices, which are measures of uncertainty in predicted secondary structure at each position in the sequence; these are then used to assign protein sequences as likely to be metamorphic vs. monomorphic (i.e. having just one-fold). We constructed a reference dataset to train our classification method, which includes a novel compilation of 140 likely-monomorphic proteins, and a set of 201 metamorphic proteins taken from the literature. Our model is able to classify proteins as metamorphic vs. monomorphic with a Matthews correlation coefficient of ∼0.4 and true positive / true negative rates of ∼65% / 80%, suggesting that it is possible to predict metamorphic behavior in proteins using only sequence information. Statement of Significance This paper introduces the diversity index as a descriptor to distinguish metamorphic proteins, which possess multiple stable folds, from monomorphic proteins that possess only one fold. The diversity index is designed to measure uncertainty in computationally predicted secondary structure, which we hypothesize is elevated for metamorphic proteins. We tested our hypothesis by training a binary classifier using the diversity index and an annotated dataset of metamorphic and monomorphic proteins, and found an optimal Matthews correlation coefficient of 0.4, supporting the hypothesis and demonstrating for the first time that it is possible to predict metamorphic behavior in proteins using only sequence information. The sequence-based classifier has broader applicability compared to methods that rely on making comparison to experimentally measured structures.

Christian Anfinsen was awarded a Nobel Prize in Chemistry in 1972 for his work on the apparent one-to-one relationship between the amino acid sequence of a protein and its three-dimensional fold, 1,2 giving rise to the classic paradigm: "one sequence, one fold". However, serendipitous discoveries in the past few decades have led to the identification of "metamorphic proteins" 3 that have the ability to jump reversibly between two distinctly different folds under native conditions. These proteins are fundamentally different 4 from intrinsically disordered proteins 5 (IDPs), morpheeins, 6 and moonlight proteins 7, 8 which have been studied for a long time. Typical conformational changes in proteins often involve "shearing" or "hinge" behavior where entire protein subunits or secondary structure elements undergo relative motions without significantly altering the fold of the protein. 9, 10 In contrast, the different folds/structures of a metamorphic protein are dissimilar on a more fundamental level, often involving changes such as the transformation of a whole-helix into a -strand (Fig. 1) . In this paper, we use significant change in secondary structure as the key defining characteristic of metamorphic proteins.

Although the number of known examples of metamorphic proteins such as IscU 11 , RfaH 12,13 , Selecase 14 , Mad2 15, 16 , Lymphotactin 17 , CLIC1 18 , KaiB 19, 20 is relatively small, it is anticipated to increase steadily and populate the "Metamorphome". In all these metamorphs, the transition from one-fold to another takes place in response to environmental triggers like pH, temperature, salt concentrations, binding partners, redox state, or oligomerization. Uncovering the metamorphome is crucial as it is expected have a transformative effect on long-held concepts of protein structure and function. It could also lead to engineering of metamorphic proteins, which are molecular switches, to act as sensors of small molecules or local environmental changes.

Traditional X-ray crystallography techniques, which account for solving 90% of the protein structures in the Protein Databank (PDB), are limited in their ability to identify metamorphism in proteins. These methods trap the protein in a minimum free energy structure in a specific crystallographic environment, thus they do not reveal the existence of alternate folds if the protein is metamorphic. A powerful method to detect protein metamorphism is solution-state NMR. However, high-throughput screening protein sequences for potential metamorphic behavior by NMR is not feasible. A realistic approach would be to identify metamorphic candidates using computational approaches, which would allow experimental verification to focus on a smaller set of candidate proteins. A recent computational study from Porter and Looger 21 identified 96 fold-switching candidates in the Protein Data Bank. The study stated that two characteristics of metamorphic proteins include discrepancies between experimentally derived and computationally predicted secondary structures, and the occurrence of multiple independent subdomains that each fold cooperatively. Using these two metrics, they estimated that up to 4% of the proteins in the PDB may be metamorphic, which suggests that this class of proteins appears to be more common than those identified so far.

In this work, we propose a novel binary classifier for predicting protein metamorphism based on the diversity index, which takes advantage of the uncertainty in secondary structure prediction methods. This method has a unique advantage that it can predict metamorphic behavior in a protein of interest purely based on the amino acid sequence, without requiring a priori experimental knowledge of the three-dimensional structure. The classification method is trained using two reference datasets consisting of ~200 manually annotated monomorphic and metamorphic sequences respectively. We found robust performance of the diversity index-based classifier with a Matthews correlation coefficient of ~0.4 (corresponding to ~70% accuracy) that is largely insensitive to changes in the parameterization and training dataset.

The rest of this paper is organized as follows. We first give a brief overview of secondary structure prediction (SSP) methods, as they provide the essential inputs into our classifier. Next, we introduce the diversity index (DI) which measures the uncertainty of predicted secondary structure, and we outline how the DI is used to classify a protein sequence as metamorphic or monomorphic. This is followed by a description of the reference datasets containing known metamorphic and monomorphic sequences used to train our classifier. The performance of the classifier is discussed in detail using metrics such as the Matthews correlation coefficient, true positive rates, and true negative rates, and its robustness is tested using randomized crossvalidation, sensitivity analysis, and examining how performance varies with different input SSP programs. We include a discussion of "outlier" protein sequences that are consistently misclassified by the DI-based model, as well as how the performance depends on the sequence database for position-specific scoring matrix generation, an important auxiliary input for the SSP programs. The paper concludes with some promising future directions.

Secondary Structure Prediction (SSP). Secondary structure (SS) is a property of amino acid residues within a protein structure that describes its local intrachain three-dimensional structure.

Under the well-known DSSP system, 22 secondary structure may be classified into eight states, which can be further reduced down to three: α-helix (H), β-sheet (E), and random coil (C). In the years since the introduction of SS classification for known protein structure, several data-driven computational methods have emerged for secondary structure prediction (SSP) using only primary structure information, i.e. the amino acid sequence. Today, SSP is a vital part of the modern toolkit for protein structure prediction and design.

SSP methods can be understood in the conceptual framework of machine learning. The protein sequence is first processed into a feature vector consisting of information with structural relevance. Such features may include a PSSM (position-specific scoring matrix), which estimates the probability distribution of amino acid residues at each position in the sequence, and is computed by performing sequence alignments to a sequence database 23 using programs such as PSI-BLAST 24 . The feature vector is input into a neutral network model, which has significant flexibility in its internal architecture, and provides three outputs representing the relative probabilities of helix (H), sheet (E), and random coil (C) at each position. The parameters of the neural network are trained to reproduce known secondary structures from widely available structural datasets. The accuracy of three-state SSP for modern methods has been reported to be as high as 82-84%. 25 In this paper, four widely used SSP programs were applied to predict the secondary structure of every sequence in our datasets, namely Psipred 26 , SPIDER2 27 , SPIDER3 28 , and Porter 5.0, denoted here as Porter5 29 . Psipred, developed in 1999, introduced the idea of using the PSSM generated by PSI-BLAST as input to a neural network for secondary structure prediction. SPIDER2 uses a deep neural network that incorporates the PSSM from PSI-BLAST along with amino acid physicochemical properties 30 to predict secondary structures and main chain dihedral angles. SPIDER3 is an updated version of SPIDER2 that incorporates hidden Markov model sequence profiles generated by the HHBlits program 31 as input to a bidirectional recurrent neural network architecture, effectively allowing the entire sequence to calculate SS prediction at each position instead of a sliding window as in SPIDER2. Porter5 is the latest version of a series of SSP programs and uses HHBlits-generated HMM sequence profiles and PSI-BLAST-generated PSSMs as input. In this paper, we used the UniProt90_2019_01 sequence database as the input to PSI-BLAST for PSSM generation, and the Uniclust30_2018_08 database was used as input for HHBlits. These published sequence alignment databases are distinct from the metamorphic and monomorphic reference datasets that were compiled as part of this work.

Metamorphic Proteins and Diversity Index. Metamorphic proteins can reversibly adopt multiple folded conformations for the same amino acid sequence under native conditions. 3, 4 Moreover, representative examples of metamorphic proteins are characterized by significant differences in secondary structure between folds (Figure 1 ), which is a distinct feature from more typical kinds of conformational change that generally preserve secondary structure as described in the Introduction. Because these metamorphic proteins possess multiple stable folds with differences in secondary structure, our central hypothesis is that metamorphic protein sequences are able to "confuse" secondary structure prediction programs. According to this hypothesis, we defined one descriptor, the diversity index (DI):

where P(E), P(H) and P(C) are output quantities from the SSP program representing the probabilities of strand, helix and coil, respectively, for a single residue in the sequence. The DI for a residue ranges from 1 to 3. When the value is close to 1, it means the SSP program has a high degree of confidence in the predicted SS, whereas the predicted SS is less certain when the DI value is close to 3. Because metamorphic proteins tend to have contiguous portions of the sequence (or even the whole protein) that undergo changes in secondary structure, we also hypothesized that the DI of metamorphic protein sequences will be elevated in contiguous regions of the sequence. Therefore, we consider the maximum value of a moving average of the DI over the sequence as the main criterion to predict metamorphic behavior in a protein sequence.

In other words, a sequence will be classified as metamorphic if the following criterion is satisfied:

where the "number of consecutive residues" and ℎ "diversity index threshold" are adjustable parameters. This binary classifier needs to be trained on reference or "manually annotated" datasets consisting of known-metamorphic and known-monomorphic (i.e. single fold)

sequences. We will describe the construction of these datasets in the following sections.

Construction of the metamorphic reference dataset.

In 2018, Porter et al published a paper listing 192 (96 pairs) of existing metamorphic proteins 21 in which most pairs have very high sequence similarity to one another (between 90% and 100%).

Our metamorphic reference dataset, listed in Supplementary Table S1 , makes the following revisions to the listing in Ref. 21 . In eight cases, both folds of a metamorphic protein existed as different chains with identical sequences in a single structure, and these sequences are counted twice in our dataset (e.g. 5C1V). Among the original set of structures, one protein is no longer available from the database (PDB ID: 2A01). We also removed proteins where the fold switching region is contained within 20 residues of the N-and C-termini (4ZRB, counted twice)

or if the sequence length is shorter than 40 residues (4FU4, 4G0D, 5K5G and 2KB8); this is because our classifier requires taking a moving average of the diversity index, requiring an sequence that is longer than the largest window size (>15 residues) plus the number of the removed terminal residues (>5*2 residues). In total, 8 proteins were removed from the list for the reasons above. We also added several proteins to the list, including the known metamorphic protein GABA that was excluded from Ref. 21 Construction of the monomorphic reference dataset. Our classification model for predicting protein metamorphism needs to be trained on proteins with known metamorphic behavior, as well as those with known single-fold (i.e. monomorphic) behavior. Although it is widely assumed that the PDB contains mostly monomorphic proteins, it is likely that a significant portion exhibits as-yet undiscovered metamorphic behavior. Therefore, we queried the PDB to obtain a set of protein structures that are highly likely to be monomorphic based on the following set of criteria: (1) The structure should be reported at least 10 years ago and has a good quality structure, in the sense that X-ray structures with resolution > 2.2 Å were filtered out; (2) there must be >30 published structures with at least 50% sequence similarity with the structure of interest; (3) the sequence length is > 40 and < 250 residues, in order to meet the criteria of having a well-folded core while staying within the typical sequence lengths of globular proteins.

Each structure found in the above manner is termed 'parent protein', and structures with high sequence similarity found in step (2) above are termed 'child proteins'. A total of 1387 'parent protein' structures with a maximum sequence similarity of 70% and more than 65000 'child protein' structures were downloaded along with their abstracts from the RCSB PDB web server using an automated crawler written in Python that uses the scrapy package. Two filtering rules were imposed in order to maximize the probability that a 'parent protein' is monomorphic:

(1) The root-mean-square deviation (RMSD) values were calculated for all pairs of structures after sequence alignment for a 'parent protein' and all of its 'children'. The structure was excluded from the data set if any of the pairwise RMSD values exceeded 2.4 Å.

(2) The mismatch in secondary structure (SS) was calculated for all pairs of structures after sequence alignment with a 'parent protein' and all of its 'children'. A positional mismatch score is calculated by summation over aligned residues in a window of 30 9 residues in length, where "2" was assigned if one sequence is H and the other is E, and "1" was assigned if one sequence is C and the other is either E or H, then taking the maximum value over all window positions. The structure was excluded from the data set if the SS mismatch score between any pair of sequences exceeded 9.

Finally, the abstracts of the corresponding publications were checked for keywords such as 'foldswitching', 'metamorphic', 'two-folds', and other synonyms; if the abstract indicated possible metamorphic behavior, then it was excluded from this dataset as well. This procedure resulted in a total of 140 likely monomorphic proteins (Supplementary Table S2 ), including 4 proteins that we deemed to be monomorphic from reviewing the literature but did not meet the above criteria.

An example of a metamorphic protein (KaiB) and a monomorphic protein (1AB9) from our reference datasets is shown in Figure 2 . The sensitivity of our model was tested by cross-validation. In each of 10 trials, the reference metamorphic and monomorphic data sets were each randomly divided into training and test sets comprising 85% and 15% of the data, respectively. The parameters were determined by maximizing the MCC for the training set, then these parameter values were used to calculate the MCC for the test set. Figure 4 and least one independent folding domain with a 3-D structure that is largely independent of the rest of the sequence and proposed a method to predict metamorphic behavior based on prediction of independent folding domains. 21, 34 This method uses the protein's 3D structure as essential input data, and thus its predictions are based on existing structural knowledge.

More recently, Porter et al and coworkers reported that metamorphic proteins have lower SSP accuracy than monomorphic proteins or fragments, 33 which is similar to the ideas in our current work; however, the method they proposed requires prior knowledge of experimental secondary structure. A major differentiating feature of the diversity index-based classification method presented here is that it requires no experimental data for the sequence of interest. Thus, this method could be used to make predictions of metamorphism in protein sequences where there is no existing structural data.

We also examined the possibility of obtaining an improved classification model based on a linear combination of DIs obtained from two SSP programs, essentially increasing the number of descriptors to two. The discriminant parameters (i.e. slope and intercept of the line) were optimized by maximizing the MCC. 35 Using a linear combination of the SPIDER2 DI and the Porter5 DI, we found the MCC value of the optimal model increases to 0.45. Figure Figure S2 , 2LQW is a key signaling protein that exists as a monomer while 2BZY is a partial structure of a CrkL homo-dimer protein, in which the existing part has similar SS to 2LQW. Moreover, the truncated CrkL monomer protein (PDB ID 2BZX) has highly similar secondary structure to 2BZY. The high similarity in secondary structure is consistent with the classification assigned by our method, which is based on differences in secondary structure between folds.

2. 2NNT 38 :2MWF 39 . 2NNT is a tetramer amyloid protofilament that forms an extended βsheet between multiple chains, whereas 2MWF is a mutant monomer that forms a β-sheet within the residues in one chain. Again, the highly similar secondary structure in both folds is consistent with the classification assigned by our method.

3. 4HDD 40 :2LEP 41 . The structures in this pair are similar in terms of secondary structure but have a large RMSD. 4HDD is a homodimer in which a β-sheet is formed between chains, whereas 2LEP uses the same domain to form a β-sheet within one chain.

4. 1G2C 42 is a truncated protein whose SS closely matches with the corresponding residues in 5C6B, which is a full structure. Strikingly, the other protein 5C6B 36 4. 3CHB is another labeled monomorphic protein whose DIs are very large in all the SSP programs. Unlike the other two false positive proteins above, 3CHB 48 has a long α-helix and five medium length β-sheets. Another short length α-helix is located at the Nterminal. According to the SPIDER2 prediction, three out of five β-sheets have relatively large DIs, resulting to a region with high average DI value. So far, we do not have a good explanation for the reason of this false positive. One possibility is that other proteins in the PDB have highly similar sequences to these high-DI β-sheets but have different SS.

We note that it is possible for our reference monomorphic dataset to include proteins that are actually metamorphic, despite our efforts to minimize this occurrence. This is because our selection of monomorphic proteins was based on the analysis of known structures in the PDB, which by definition excludes alternate folds or structures that have not yet been discovered or deposited.

Dependence of results on sequence database. The performance of SSP programs relies on the non-redundant sequence database that is used to compute the position-specific scoring matrix. Figure 6 shows the differences in classification performance when SPIDER2 is used as the SSP program for different choices of the non-redundant sequence database. The Uniref-50, Uniref-90

and Uniref-100 databases have a progressively larger number of sequences and sequence identity among pairs of sequences. Figure 6 shows that Uniref-50 has markedly lower performance for our classifier compared to Uniref-90 and Uniref-100, and it is currently unclear whether the poor classification performance is due to the smaller size of the sequence dataset or the more stringent threshold on sequence identity. Surprisingly, the modified non-redundant sequence dataset 49 from I-TASSER (PSSpred) gives a very high MCC value (0.457), even though it was released in 2014 and has not been continually updated as the other three. Thus, the DI-based classification performance depends on the sequence database in a nontrivial way and does not necessarily yield improved results for updated database versions. In this paper we described a diversity index-based classification model to predict metamorphic behavior in proteins solely based on the protein sequence. Our model was trained on a reference dataset consisting of 140 known monomorphic proteins and 201 known metamorphic proteins.

Although the main purpose of SSP programs is to predict secondary structure, our results indicate that the "byproducts" of SSP, namely the alternate SS probabilities and the derived diversity index, can play a key role for predicting metamorphism in proteins. Among the four popular SSP programs, SPIDER2 has the overall best performance and robustness in classifying proteins as monomorphic vs. metamorphic. Further improvements in performance may be obtained by comparing the output of multiple SSP programs. All of these results indicate that the diversity index is a key variable to represent the metamorphic property of proteins. Because all four SSP programs give similar MCC values when used in classification to within ~10%, we think further improvements in predicting protein metamorphism will require SSP methods that focus more on accurate quantification of uncertainty rather than yielding the best fit to experimental data. 

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The Kinetics of Formation of Native Ribonuclease During Oxidation of the Reduced Polypeptide Chain

Unfolding the Mysteries of Protein Metamorphosis

Structural studies of p2lWa1CiPl/Sdil in the free and Cdk2-bound state: Conformational disorder mediates binding diversity

Morpheeins -a new structural paradigm for allosteric regulation

Gene sharing by 6-crystallin and argininosuccinate Iyase

Moonlighting proteins

Structural Mechanisms for Domain Movements in Proteins

Binding Leverage as a Molecular Basis for Allosteric Regulation

Metamorphic Protein IscU Changes Conformation by cis -trans Isomerizations of Two Peptidyl-Prolyl Peptide Bonds

Evolution of multisubunit RNA polymerases in the three domains of life

An α Helix to β Barrel Domain Switch Transforms the Transcription Factor RfaH into a Translation Factor

Multiple Stable Conformations Account for Reversible Concentration-Dependent Oligomerization and Autoinhibition of a Metamorphic Metallopeptidase

Protein Metamorphosis: The Two-State Behavior of Mad2

The Mad2 Conformational Dimer: Structure and Implications for the Spindle Assembly Checkpoint

Interconversion between two unrelated protein folds in the lymphotactin native state

The Intracellular Chloride Ion Channel Protein CLIC1 Undergoes a Redox-controlled Structural Transition

A protein fold switch joins the circadian oscillator to clock output in cyanobacteria

Structural basis of the day-night transition in a bacterial circadian clock

Extant fold-switching proteins are widespread

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Inaccurate secondary structure predictions often indicate protein fold switching

A thermodynamic definition of protein domains

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

Domain organization differences explain Bcr-Abl's preference for CrkL over CrkII

The C-Terminal SH3 Domain of CRKL as a Dynamic Dimerization Module Transiently Exposing a Nuclear Export Signal

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Folding kinetics of WW domains with the united residue force field for bridging microscopic motions and experimental measurements

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Activity-Based Protein Profiling of the Escherichia coli GlpG Rhomboid Protein Delineates the Catalytic Core

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This research was supported by the U.S. Army Research Office Award #W911NF-17-1-0434.We would like to acknowledge Archana Chavan, Xuejun Yao, and Yudong Qiu for valuable discussions.