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Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods^{ †}

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## Abstract

**:**

## 1. Introduction

## 2. Computational Protein Design

`ref2015`and

`beta_nov_16`score functions [4,20] also integrate rotamer log-probabilities of apparition in natural structures, as provided in the Dunbrack library, in a specific energy term. To be optimized, the energy function should be easy to compute while remaining as accurate as possible, to predict relevant sequences. To try to meet these requirements, additive pairwise decomposable approximations of the energy have been chosen for protein design approaches [6,21]. The decomposable energy E of a sequence-conformation $\mathbf{r}=({r}_{1},\cdots ,{r}_{n})$ where ${r}_{i}$ is the rotamer used at the position i in the protein sequence can be written as:

## 3. CPD as a Weighted Constraint Satisfaction Problem

**Φ**is a set of potential functions. A potential function ${\phi}_{\mathbf{S}}$ maps ${\mathbf{D}}_{\mathbf{S}}$ to $[0,+\infty ]$. The joint potential function is defined as:

**Variables**- We add sequence variables to the network: ${\mathbf{X}}^{\prime}={\mathbf{X}}^{seq}\cup \mathbf{X}$, where ${\mathbf{X}}^{seq}=\left\{{X}_{i}^{seq}|{X}_{i}\in \mathbf{X}\right\}$. The value of ${X}_{i}^{seq}$ represents the amino acid type of the rotamer value of ${X}_{i}$.
**Domains**- $\mathbf{D}={\mathbf{D}}^{seq}\cup \mathbf{D}$ where ${\mathbf{D}}^{seq}=\left\{{\mathbf{D}}_{i}^{seq}\right|{\mathbf{D}}_{i}\in \mathbf{D}\}$ where the domain ${\mathbf{D}}_{i}^{seq}$ of ${X}_{i}^{seq}$ is the set of available amino acid types at position i.
**Constraints**- The new set of cost functions ${\mathbf{C}}^{\prime}$ is made of the initial functions $\mathbf{C}$; and sequence constraints that ensure that ${X}_{i}^{seq}$ is the amino acid type of rotamer ${X}_{i}$. Such a function ${c}_{{X}_{i},{X}_{i}^{seq}}$ just forbids (map to cost ⊤) pairs of values $(r,a)$ where the amino acid identity of rotamer r does not match a. All other pairs are mapped to cost 0.

## 4. Diversity and Optimality

#### 4.1. Measuring Diversity

**Definition**

**1.**

**Definition**

**2.**

**Definition**

**3.**

- its average dissimilarity: $\overline{d}\left(\mathbf{Z}\right)=\frac{2}{\left|\mathbf{Z}\right|\left(\right|\mathbf{Z}|-1)}\sum _{\mathbf{t}\ne {\mathbf{t}}^{\prime}\in \mathbf{Z}}d(\mathbf{t},{\mathbf{t}}^{\prime})$
- its minimum dissimilarity: $\stackrel{\u02c7}{d}\left(\mathbf{Z}\right)=\underset{\mathbf{t}\ne {\mathbf{t}}^{\prime}\in \mathbf{Z}}{min}d(\mathbf{t},{\mathbf{t}}^{\prime})$

**Definition**

**4.**

#### 4.2. Diversity Given Sequences of Interest

- A native functional sequence ${\mathbf{s}}_{nat}$ is known for the target backbone. The designer wants that less than ${\delta}_{nat}$ mutations be introduced on some sensitive region of the native protein, to avoid disrupting a crucial protein property.
- A patented sequence ${\mathbf{s}}_{pat}$ exists for the same function, and sequences with more than ${\delta}_{pat}$ mutations are required for the designed sequence to be usable without requiring a license.

- ${\mathrm{D}}_{\mathrm{IST}}({\mathbf{X}}^{seq},{\mathbf{s}}_{nat},H,-{\delta}_{nat})$
- ${\mathrm{D}}_{\mathrm{IST}}({\mathbf{X}}^{seq},{\mathbf{s}}_{pat},H,{\delta}_{pat})$

#### 4.3. Sets of Diverse and Good Quality Solutions

**Definition**

**5**

**.**Given a dissimilarity matrix D, an integer M and a dissimilarity threshold δ, the problem DiverseSet$(\mathcal{C},D,M,\delta )$ consists of producing a set $\mathbf{Z}$ of M solutions of $\mathcal{C}$ such that:

**Diversity**- For all $\mathbf{t}\ne {\mathbf{t}}^{\prime}\in \mathbf{Z}$, $\mathtt{d}(\mathbf{t},{\mathbf{t}}^{\prime})\u2a7e\delta $, i.e., ${\mathrm{D}}_{\mathrm{IST}}(\mathbf{t},{\mathbf{t}}^{\prime},D,\delta )=0$.
**Quality**- The solutions have minimum cost, i.e., $\underset{\mathbf{t}\in \mathbf{Z}}{\sum {}^{\top}}{C}_{\mathcal{C}}\left(\mathbf{t}\right)$ is minimum.

**Definition**

**6**

- The first solution $\mathbf{Z}\left[1\right]$ is the optimum of $\mathcal{C}$
- When solutions $\mathbf{Z}[1..(i-1\left)\right]$ are computed, $\mathbf{Z}\left[i\right]$ is such that:for all $1\u2a7dj<i,{\mathrm{D}}_{\mathrm{IST}}\left(\mathbf{Z}\right[i],\mathbf{Z}[j],D,\delta )=0$ and $\mathbf{Z}\left[i\right]$ has minimum cost.That is, $\mathbf{Z}\left[i\right]$ is the minimum cost solution, among assignments that are at distance at least δ from all the previously computed solutions.

## 5. Relation with Existing Work

**Definition**

**7**

**Theorem**

**1.**

**Proof.**

**Theorem**

**2.**

- Any assignment $\mathbf{t}$ of a CFN $\mathcal{C}=(\mathbf{X},\mathbf{D},\mathbf{C})$ is a δ-mode iff it is an optimal solution of the CFN $(\mathbf{X},\mathbf{D},\mathbf{C}\cup \{{\mathrm{D}}_{\mathrm{IST}}(\mathbf{X},\mathbf{t},H,-\delta )\left\}\right)$
- For bounded δ, this problem is in P.

**Proof.**

- The function ${\mathrm{D}}_{\mathrm{IST}}(\mathbf{X},\mathbf{t},H,-\delta )$ restricts $\mathbf{X}$ to be within $\delta $ of $\mathbf{t}$. If $\mathbf{t}$ is an optimal solution of $(\mathbf{X},\mathbf{C}\cup \{{\mathrm{D}}_{\mathrm{IST}}(\mathbf{X},\mathbf{t},H,-\delta \}\left)\right)$ then there is no better assignment than $\mathbf{t}$ in the $\delta $-radius Hamming ball and $\mathbf{t}$ is a $\delta $-mode.
- For bounded $\delta $, a CFN with n variables and at most d values in each domain, there is $O\left({\left(nd\right)}^{\delta}\right)$ tuples within the Hamming ball, because from $\mathbf{t}$, we can pick any variable (n choices) and change its value (d choices), $\delta $ times. Therefore, the problem of checking if $\mathbf{t}$ is optimal is in P.

## 6. Representing the Diversity Constraint

#### 6.1. Using Automata

- The alphabet is the set of possible values, i.e., the union of the variable domains $\Sigma ={\bigcup}_{i=1}^{n}{\mathbf{D}}_{i}$
- The set of states $\mathbf{Q}$ gathers $(\delta +1)\xb7(n+1)$ states denoted ${q}_{i}^{d}$:$$\mathbf{Q}=\left\{{q}_{i}^{d}|0\u2a7di\u2a7dn,0\u2a7dd\u2a7d\delta \right\}$$
- In the initial state, no value of $\mathbf{X}$ has been read, and the dissimilarity is 0:$${Q}_{0}={q}_{0}^{0}$$
- The assignment is accepted if it has dissimilarity from $\mathbf{t}$ higher than the threshold $\delta $, hence the accepting state:$$\mathbf{F}=\left\{{q}_{n}^{\delta}\right\}$$
- For every value r of ${X}_{i}$, the transition function $\Delta :\mathbf{Q}\times \Sigma \times \mathbf{Q}$ defines a 0-cost transition from ${q}_{i}^{d}$ to ${q}_{i+1}^{min(d+D(r,\mathbf{t}[i+1]),\delta )}$. All other transitions have infinite cost ⊤.

#### 6.2. Exploiting Automaton Function Decomposition

#### 6.3. Compressing the Encoding

- $m{d}_{n}=0$
- For $0\u2a7di<n$, $m{d}_{i}=m{d}_{i+1}+ma{x}_{v,{v}^{\prime}\in {\mathbf{D}}_{i+1}}D(v,{v}^{\prime})$

- One variable ${X}_{\mathbf{S}}$ per constraint ${c}_{\mathbf{S}}\in \mathbf{C}$:$${\mathbf{X}}^{\prime}=\left\{{X}_{\mathbf{S}}\right|{c}_{\mathbf{S}}\in \mathbf{C}\}$$
- Domain ${\mathbf{D}}_{{X}_{\mathbf{S}}}$ of variable ${X}_{\mathbf{S}}$ is the set of tuples $\mathbf{t}\in {\mathbf{D}}_{\mathbf{S}}$ that satisfy the constraint ${c}_{\mathbf{S}}$:$${\mathbf{D}}^{\prime}=\left\{{\mathbf{D}}_{{X}_{\mathbf{S}}}\right|{c}_{\mathbf{S}}\in \mathbf{C}\}\phantom{\rule{2.em}{0ex}}{\mathbf{D}}_{{X}_{\mathbf{S}}}=\{\mathbf{t}\in {c}_{\mathbf{S}}\}$$
- For each pair of constraints ${c}_{\mathbf{S}},{c}_{{\mathbf{S}}^{\prime}}\in \mathbf{C}$ with overlapping scopes $\mathbf{S}\cap {\mathbf{S}}^{\prime}\ne \u2300$, there is a constraint ${c}_{{\mathbf{X}}_{\mathbf{S}},{\mathbf{X}}_{{\mathbf{S}}^{\prime}}}$ that ensures that tuples assigned to ${X}_{\mathbf{S}}$ and ${X}_{{\mathbf{S}}^{\prime}}$ are compatible, i.e., they have the same values on the overlapping variables:$${\mathbf{C}}^{\prime}=\left\{{c}_{{\mathbf{X}}_{\mathbf{S}},{\mathbf{X}}_{{\mathbf{S}}^{\prime}}}|{X}_{\mathbf{S}},{X}_{{\mathbf{S}}^{\prime}}\in {\mathbf{X}}^{\prime},\mathbf{S}\cap {\mathbf{S}}^{\prime}\ne \varnothing \right\}$$$${c}_{{\mathbf{X}}_{\mathbf{S}},{\mathbf{X}}_{{\mathbf{S}}^{\prime}}}=\{(\mathbf{t},{\mathbf{t}}^{\prime})\in {\mathbf{D}}_{{X}_{\mathbf{S}}}\times {\mathbf{D}}_{{X}_{{\mathbf{S}}^{\prime}}}|\mathbf{t}[\mathbf{S}\cap {\mathbf{S}}^{\prime}]={\mathbf{t}}^{\prime}[\mathbf{S}\cap {\mathbf{S}}^{\prime}]\}$$

- All the variables in $\mathbf{X}$ and the variables ${X}_{\mathbf{S}}$ from the dual network (and associated domains):$${\mathbf{X}}^{\u2033}=\mathbf{X}\cup {\mathbf{X}}^{\prime}$$
- For any dual variable ${X}_{\mathbf{S}}$, and each ${X}_{i}\in \mathbf{S}$, the set of constraints ${\mathbf{C}}^{\u2033}$ contains a function involving ${X}_{i}$ and ${X}_{\mathbf{S}}$:$${c}_{{X}_{i}{X}_{\mathbf{S}}}:(v,\mathbf{t})\in {\mathbf{D}}_{i}\times {\mathbf{D}}_{{X}_{\mathbf{S}}}\mapsto \left\{\begin{array}{cc}0\hfill & \phantom{\rule{4.pt}{0ex}}\mathrm{if}\mathbf{t}\left[{X}_{i}\right]=v\hfill \\ \top \hfill & \phantom{\rule{4.pt}{0ex}}\mathrm{otherwise}.\hfill \end{array}\right.$$

## 7. Greedy DiverseSeq

- The CFN $\mathcal{C}$ is solved using branch-and-bound while maintaining soft local consistencies [26].
- If a solution $\mathbf{t}$ is found, it is added to the ongoing solution sequence $\mathbf{Z}$.
- If M solutions have been produced, the algorithm stops.
- Otherwise, the cost function ${\mathrm{D}}_{\mathrm{IST}}(\mathbf{X},\mathbf{t},D,\delta )$ is added to the previously solved problem.
- We loop and solve the problem again (Step 1)

Algorithm 1: Incremental production of DiverseSeq$(\mathcal{C},D,M,\delta )$ |

**Incrementality**- Since the problems solved are increasingly constrained, all the equivalence preserving transformations and pruning that have been applied to enforce local consistencies at iteration $i-1$ are still valid in the following iterations. Instead of restarting from a problem $\mathcal{C}=(\mathbf{X},\mathbf{D},\mathbf{C}\cup {\bigcup}_{1\le j<i}\left\{{\mathrm{D}}_{\mathrm{IST}}(\mathbf{X},\mathbf{Z}\left[j\right],D,\delta \}\right)$, we reuse the problem solved at iteration $i-1$ after it has been made locally consistent, add the ${\mathrm{D}}_{\mathrm{IST}}(\mathbf{X},\mathbf{Z}[i-1],D,\delta )$ constraint and reinforce local consistencies. As with incremental SAT solvers, adaptive variable ordering heuristics that have been trained at iteration $i-1$ are reused at iteration i.
**Lower bound**- Since the problems solved are increasingly constrained, we know that the optimal cost $o{c}^{i}$ obtained at iteration i cannot have a lower cost than the optimum cost $o{c}^{i-1}$ reported at iteration $i-1$. When large plateaus are present in the energy landscape, this allows stopping the search as soon as a solution of cost $o{c}^{i-1}$ is reached, avoiding a useless repeated proof of optimality.
**Upper bound prediction**- Even if there are no plateaus in the energy landscape, there may be large regions with similar variations in energy. In this case, the difference in energy between $o{c}^{i-1}$ and $o{c}^{i}$ will remain similar for several iterations. Let ${\Delta}_{i}^{h}={max}_{max(2,i-h)\le j<i}(o{c}^{j}-o{c}^{j-1})$ be the maximum variation observed in the last h iterations (we used $h=5$). At iteration i, we can first solve the problem with a temporary upper bound ${k}^{\prime}=min(k,o{c}_{i-1}+2.{\Delta}_{i}^{h})$ that should preserve a solution. If ${k}^{\prime}<k$, this will lead to increased determinism, additional pruning, and possibly exponential savings. Otherwise, if no solution is found, the problem is solved again with the original upper bound k. We call this predictive bounding.

## 8. Results

`-A`in toulbar2). The computational cost of VAC, although polynomial, is high, but amortized over the M resolutions. During tree search, the default existential directional arc consistency (EDAC) was used. All experiments were performed on one core of a Xeon Gold 6140 CPU at 2.30 GHz. Wall-clock times could be further reduced using a parallel implementation of the underlying Hybrid Best-First search engine [45], currently under development in

`toulbar2`.

`ref2015`score function [48]. Alternate rotamer libraries and score functions can be used if required as the algorithms presented here are not specialized for Rosetta (and not even for CPD, see [44]). The resulting networks have from 44 to 87 rotamer variables, and maximum domain sizes range from 294 to 446 rotamers. The number of variables is doubled after sequence variables are added.

`-rgap`and

`-agap`flags respectively).

## 9. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

CFN | Cost Function Network |

CPD | Computational Protein Design |

CSP | Constraint Satisfaction Problem |

MRF | Markov Random Field |

NSR | Native Sequence Recovery |

NSSR | Native Sequence Similarity Recovery |

WCSP | Weighted Constraint Satisfaction Problem |

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**Figure 1.**An example of two protein sequences (top) where two mutable amino acids have been redesigned. At the first position, the amino acid D (an aspartic acid) has been changed to a L (leucine), in a specific conformation (orientation). At the second position, the arginine R, with its very long and flexible sidechain has been changed to a glutamine Q. The figure on the right illustrates the potential flexibility of the long arginine sidechain, showing a sample of several possible superimposed conformations, representing a fraction of all possible conformations for an arginine sidechain in existing rotamer libraries.

**Figure 2.**Input backbone and cost function network representation of a corresponding CPD instance with 6 mutable or flexible residues.

**Figure 3.**Weighted automaton representing ${\mathrm{D}}_{\mathrm{IST}}(\mathbf{X},\mathbf{t},H,\delta )$ where $\mathbf{X}$ is a set of 5 variables, with domains ${\mathbf{D}}_{i}=\{a,b,c\}$, $\mathbf{t}=aacba$, H represents the Hamming distance, and $\delta $ is set to 2. State ${q}_{i}^{d}$ means that values ${X}_{1}\cdots {X}_{i}$ are such that $H({X}_{1}\cdots {X}_{i},t[{X}_{1}\cdots {X}_{i}])=d$ (or $\u2a7e\delta $ if $d=\delta $). A labeled arrow $q\stackrel{(v,w)}{\u27f6}{q}^{\prime}$ means $\Delta (q,v,{q}^{\prime})=w$, i.e., there is a transition from q to ${q}^{\prime}$ with value v and weight w.

**Figure 4.**Hypergraph representation of the decomposition of a WRegular cost function with additional state variables ${Q}_{i}$ and transition-encoding ternary functions.

**Figure 6.**Comparison of the best NSR value obtained with ten 1-diverse sequences ($\delta =1$, blue curve) with the best NSR value obtained with libraries of ten sequences of increased diversity. Each plot corresponds to a specific additional value of $\delta $ ($\delta =2$ to 15, golden curve). Plots are ordered lexicographically from top-left to bottom-right, with increasing values of diversity ($\delta $). In each plot, the X-axis ranges over all tested backbones, sorted in increasing order of NSR value for the 1-diverse case and the Y-axis gives the corresponding NSR value. As the diversity requirement increases, the NSR value indicated by the golden curve increases also visibly.

**Figure 7.**Comparison of the best NSSR value obtained with ten 1-diverse sequences ($\delta =1$, blue curve) with the best NSSR value obtained with libraries of ten sequences of increased diversity. Each plot corresponds to a specific additional value of $\delta $ ($\delta =2$ to 15, golden curve). Plots are ordered lexicographically from top-left to bottom-right, with increasing values of diversity ($\delta $). In each plot, the X-axis ranges over all tested backbones, sorted in increasing order of NSSR value for the 1-diverse case and the Y-axis gives the corresponding NSSR value. As the diversity requirement $\delta $ increases, the NSSR value indicated by the golden curve increases also visibly.

**Figure 8.**The blue curves above give the absolute change in NSR (Y-axis,

**left**figure) and NSSR (Y-axis,

**right**figure) between the best 15-diverse and the best 1-diverse sequences found for each backbone. Backbones (on the X-axis) are ordered in increasing order of the corresponding measure. In the left figure, the bar-plot shows the difference between each backbone 1-diverse NSR and average 1-diverse NSR over all backbones. The corresponding NSR change scale appears on the right with $\pm 20\%$ labels. Red bars indicate a below-average 1-diverse NSR while blue bars indicate an above average 1-diverse NSR. The most improved NSRs, on the right of the left figure, mostly appear on weak (red, below average) 1-diverse NSRs.

**Figure 9.**Comparison of the computation times of sequence sets without diversity $\delta =1$, with sequence sets with diversity $\delta >1$. The color scale on the right indicates the corresponding value of $\delta $.

**Figure 10.**Comparison of the best NSR value obtained with ten 1-diverse sequences ($\delta =1$, blue curve) with the best NSR value obtained with libraries of ten sequences of increased diversity all predicted with an allowed gap top optimal energy of 3 kcal/mol. Each plot corresponds to a specific additional value of $\delta $ ($\delta =2$ to 15, golden curve). Plots are lexicographically ordered from top-left to bottom-right, with increasing values of diversity ($\delta $). In each plot, the X-axis ranges over all tested backbones, sorted in increasing order of NSR value for the 1-diverse case and the Y-axis gives the corresponding NSR value. As the diversity requirement $\delta $ increases, the NSR value indicated by the golden curve increases also visibly.

**Figure 11.**Comparison of the best NSSR value obtained with ten 1-diverse sequences ($\delta =1$, blue curve) with the best NSSR value obtained with libraries of ten sequences of increased diversity all predicted with an allowed gap top optimal energy of 3 kcal/mol. Each plot corresponds to a specific additional value of $\delta $ ($\delta =2$ to 15, golden curve). Plots are lexicographically ordered from top-left to bottom-right, with increasing values of diversity ($\delta $). In each plot, the X-axis ranges over all tested backbones, sorted in increasing order of NSSR value for the 1-diverse case and the Y-axis gives the corresponding NSSR value. As the diversity requirement $\delta $ increases, the NSSR value indicated by the golden curve increases also visibly.

**Figure 12.**Comparison of the computation times of sequence sets without diversity $\delta =1$, with suboptimal sequence sets with diversity $\delta >1$. An energy gap of 3 kcal/mol is allowed for actual optimum.

**Table 1.**List of protein structures used in our benchmark set, for full redesign: pdb identifier, domain length n (number of variables in the resulting CFN) and maximum domain size d.

PDB ID | n | d | PDB ID | n | d | PDB ID | n | d |
---|---|---|---|---|---|---|---|---|

1aho | 56 | 378 | 3i8z | 50 | 354 | 1ten | 81 | 392 |

2fjz | 53 | 324 | 2cg7 | 82 | 380 | 1ucs | 60 | 342 |

1b9w | 78 | 386 | 3rdy | 65 | 396 | 2bwf | 69 | 347 |

2gkt | 45 | 357 | 2erw | 47 | 446 | 2evb | 68 | 323 |

1f94 | 53 | 386 | 3vdj | 67 | 391 | 2o37 | 60 | 386 |

2pne | 77 | 401 | 2fht | 64 | 346 | 2o9s | 48 | 327 |

1hyp | 66 | 385 | 1bxy | 52 | 384 | 3f04 | 87 | 356 |

2pst | 61 | 357 | 1ctf | 68 | 349 | 3fym | 70 | 348 |

1uln | 66 | 367 | 1czp | 76 | 373 | 3gqs | 67 | 344 |

1uoy | 56 | 337 | 1fqt | 85 | 377 | 3gva | 87 | 348 |

2ca7 | 44 | 348 | 1guu | 47 | 350 | 3i2z | 67 | 360 |

1yzm | 46 | 294 | 1t8k | 68 | 361 |

**Table 2.**p-values for a unilateral Wilcoxon signed-rank test comparing the sample of best NSR (resp. NSSR) for each $\delta =2\cdots 15$ with $\delta =1$, for optimal and suboptimal (3 kcal/mol allowed energy gap to real optimum) resolution.

Exact Resolution | Subopt. Resolution | |||
---|---|---|---|---|

$\mathbf{\delta}$ | NSR | NSSR | NSR | NSSR |

2 | $2.88\times {10}^{-3}$ | $1.10\times {10}^{-3}$ | $4.11\times {10}^{-1}$ | $1.35\times {10}^{-1}$ |

3 | $3.87\times {10}^{-4}$ | $1.01\times {10}^{-4}$ | $1.14\times {10}^{-1}$ | $2.60\times {10}^{-2}$ |

4 | $4.42\times {10}^{-5}$ | $6.58\times {10}^{-5}$ | $4.48\times {10}^{-3}$ | $1.06\times {10}^{-3}$ |

5 | $8.11\times {10}^{-5}$ | $1.54\times {10}^{-5}$ | $1.98\times {10}^{-3}$ | $2.15\times {10}^{-3}$ |

6 | $1.51\times {10}^{-5}$ | $4.39\times {10}^{-6}$ | $7.47\times {10}^{-5}$ | $4.49\times {10}^{-5}$ |

7 | $1.88\times {10}^{-5}$ | $4.23\times {10}^{-6}$ | $8.86\times {10}^{-6}$ | $3.50\times {10}^{-5}$ |

8 | $1.27\times {10}^{-5}$ | $1.49\times {10}^{-6}$ | $8.19\times {10}^{-6}$ | $1.77\times {10}^{-5}$ |

9 | $2.76\times {10}^{-5}$ | $5.97\times {10}^{-6}$ | $2.07\times {10}^{-5}$ | $2.80\times {10}^{-6}$ |

10 | $1.14\times {10}^{-5}$ | $1.18\times {10}^{-5}$ | $1.78\times {10}^{-5}$ | $3.06\times {10}^{-5}$ |

11 | $4.27\times {10}^{-5}$ | $5.81\times {10}^{-7}$ | $2.32\times {10}^{-5}$ | $1.73\times {10}^{-5}$ |

12 | $6.63\times {10}^{-5}$ | $2.26\times {10}^{-6}$ | $1.75\times {10}^{-5}$ | $1.18\times {10}^{-5}$ |

13 | $4.43\times {10}^{-5}$ | $2.52\times {10}^{-6}$ | $2.48\times {10}^{-6}$ | $6.15\times {10}^{-6}$ |

14 | $2.29\times {10}^{-5}$ | $5.76\times {10}^{-6}$ | $5.26\times {10}^{-6}$ | $3.89\times {10}^{-7}$ |

15 | $3.92\times {10}^{-5}$ | $1.58\times {10}^{-6}$ | $2.68\times {10}^{-5}$ | $4.86\times {10}^{-5}$ |

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**MDPI and ACS Style**

Ruffini, M.; Vucinic, J.; de Givry, S.; Katsirelos, G.; Barbe, S.; Schiex, T.
Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods. *Algorithms* **2021**, *14*, 168.
https://doi.org/10.3390/a14060168

**AMA Style**

Ruffini M, Vucinic J, de Givry S, Katsirelos G, Barbe S, Schiex T.
Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods. *Algorithms*. 2021; 14(6):168.
https://doi.org/10.3390/a14060168

**Chicago/Turabian Style**

Ruffini, Manon, Jelena Vucinic, Simon de Givry, George Katsirelos, Sophie Barbe, and Thomas Schiex.
2021. "Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods" *Algorithms* 14, no. 6: 168.
https://doi.org/10.3390/a14060168