A Simple yet Effective $\Delta\Delta G$ Predictor is An Unsupervised Antibody Optimizer and Explainer
Authors: Lirong Wu
Yunfan Liu
Haitao Lin
Yufei Huang
Guojiang Zhao
Zhifeng Gao
Stan Z. Li
Venue: ICLR 2025 Poster (2025)
Keywords: Mutation Effect Prediction
Mutation Preference Explanation
Unsupervised Protein Evolution
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: The proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and selects them. The discovery of functionally promising mutations is challenged by the limited evolutionary accessible regions, i.e., only a small region on the fitness landscape is beneficial. There have been numerous priors used to constrain protein evolution to regions of landscapes with high-fitness variants, among which the change in binding free energy ($\Delta\Delta G$) of protein complexes upon mutations is one of the most commonly used priors. However, the huge mutation space poses two challenges: (1) how to improve the efficiency of $\Delta\Delta G$ prediction for fast mutation screening; and (2) how to explain mutation preferences and efficiently explore accessible evolutionary regions. To address these challenges, we propose a lightweight $\Delta\Delta G$ predictor (Light-DDG), which adopts a structure-aware Transformer as the backbone and enhances it by knowledge distilled from existing powerful but computationally heavy $\Delta\Delta G$ predictors. Additionally, we augmented, annotated, and released a large-scale dataset containing millions of mutation data for pre-training Light-DDG. We find that such a simple yet effective Light-DDG can serve as a good unsupervised antibody optimizer and explainer. For the target antibody, we propose a novel Mutation Explainer to learn mutation preferences, which accounts for the marginal benefit of each mutation per residue. To further explore accessible evolutionary regions, we conduct preference-guided antibody optimization and evaluate antibody candidates quickly using Light-DDG to identify desirable mutations. Extensive experiments have demonstrated the effectiveness of Light-DDG in terms of test generalizability, noise robustness, and inference practicality, e.g., 89.7$\times$ inference acceleration and 15.45\% performance gains over previous state-of-the-art baselines. A case study of SARS-CoV-2 further demonstrates the crucial role of Light-DDG for mutation explanation and antibody optimization.

Published as a conference paper at ICLR 2025
A SIMPLE YET EFFECTIVE AAG PREDICTOR IS AN UN- SUPERVISED ANTIBODY OPTIMIZER AND EXPLAINER
Lirong Wu! Yunfan Liu', Haitao Lin Yufei Huang', Guojiang Zhao?, ZZifeng Stann
Stan Z. Li
Westlake University, "DP Technology {wulirong, liuyunfan, linhaitao, huangyufei, stan zq. 11 1iJevesttakk..dd. {zhaog), gaozf)edp.tech
ABSTRACT
The proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and selects them. The discovery of functionally promising mutations is challenged by the limited evo- lutionary accessible regions, i.e., only small region on the fitness landscape is beneficial. There have been numerous priors used constrain protein evolution regions of landscapes with high-fitness variants, among which the change in bind- ing free energy (AAG) of protein complexes upon mutations is one of the most commonly used priors. However, the huge mutation space poses two challenges: (1) how to improve the efficiency of AAG prediction for fast mutation screen- ing; and (2) how to explain mutation preferences and efficiently explore accessi- ble evolutionary regions. To address these challenges, we propose lightweight AAG predictor (Light-DDG), which adopts structure-aware Transformer as the backbone and enhances it by knowledge distilled from existing powerful but com- putationally heavy AAG predictors. Additionally, we augmented, annotated, and released a large-scale dataset containing millions of mutation data for pre-training Light-DDG. We find that such simple yet effective Light-DDG can serve as good unsupervised antibody optimizer and explainer. For the target antibody, we propose novel Mutation Explainer to learn mutation preferences, which accounts for the marginal benefit of each mutation per residue. To further explore acces- sible evolutionary regions, conduct interrence-giderr antibody optimization and evaluate antibody candidates quickly using Light-DDG to identify desirable mutations. Extensive experiments have demonstrated the effectiveness of Light- DDG in terms of test generalizability, noise robustness, and inference practicality. e.g., 89.7x inference accelcration and 15.45% performance gains over previous wate-of-the-an art baselines. A case study of SARS-CoV-2 further demonstrates the crucial role of Light-DDG for mutation explanation and antibody optimization. Codes are available in Github, and an online Platform is available for researchers.
INTRODUCRTION
Proteins usually interact with other proteins form protein complexes that perform specific func- tions in biological processes (Hu et al., 2021; ; Wu et al., 2024b). A representative example is anti- body, a -ssapped protein that protects the host by binding a specific antigen, whose binding func- tion's is mainly determined by Complementary Determining Regions (CDRs) in the antibody (Murphy & Weaver, 2016). In practice, how mutate the amino acids on the interaction surface and select fa- vorable mutations are two fundamental aspects of antibody optimization. There have been many an- tibody design methods proposed, such as MEAN (Kong al., 2022), RefineGNN (Jin al., 2021), 1), and dyMEAN (Kong et al., 2023), which train conditional antibody generators on large amounts of antibody-antigen complexes and then optimize antibodies by applying Iterative Target Augmenta- tion (ITA) algorithm (Yang et al., 2020b) to fine-tune the generators. Despite the great success in conditional generation for mutations, how to build an efficient evolutionary selection sieve (Hayes al., 2024) for fast screening of mutations remains under-explored. In this paper. shift the research focus from generating to selecting mutations and indirectly explore the underlying fitness landscape by focusing on regions where AAGs over mutations are minimized. We demonstrate that

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Published as a conference paper at ICLR 2025
even a simple but effective AAG predictor can serve as good unsupervised antibody optimizer and explainer, which doesn't 't require any additional functional annotations and deep generative models.
A huge challenge for protein optimization is the enormous combinatorial space of over 20 potential mutations, where N is the number of mutable sites. Therefore, two aspects need to be considered in the design: (1) how to develop a simple but effective AAG predictor for fast sereening of candidate mutations in a relatively short time; (2) how to explain mutation preferences and efficiently search for accessible evolutionary paths, i.e. promising mutations, from the enormous combinatorial space.
Recently, unsupervised anergy-based models (Jin et al., 2023; Luo et al., 2023) have revealed that the log-likelihood of protein complexes is highly correlated with experimental binding energy, making AAG one of the suitable priors for guiding protein evolution. Early computational approaches for AAG prediction are mainly biophysics- based (Alford et al., 2017; Park et al., 2016; Delgado al., 2019) or statistics-based (Geng et al., 2019; Li et al., 2016), which are limited either in efficiency or effectiveness. Recently, many deep learning-base -baeed techniques have been proposed, most of which tackle the scarcity of annotated experimental data by pre-training variety of pretext tasks, including Masked Inverse Folding, Rotamer Density Estimation (RDE) (Luo et al., 2023), Side-chain Diffusion (Diff (DiffAffinity) (Yang et al., 2022), Multi-level Interaction Mod- cling (ProMIM) (Mo et al., 2024). Another state-of-the- thettrrrrrrt AAG predictor is Prompt-DDG (Wu et al., 2024c), which flexibly provides wild-type and mutated complexes with their microenviron- mental differences around each mutation. Despite the great advances, the architectural complexity of these methods burdens inference, which is largely due to their reliance on the IPA-style backbone as in AlphaFold2 (Jumper et al., 2021), which encodes local and global coordinates at each layer.
massive unlabeled data using
To develop a simple yet effective AAG predic- for (Light-DDG), it requires the fulfillment of both efficiency and effectiveness. For the goal of high inference efficiency, we simplify the architecture to lightweight Transformer and achieve model compression by knowledge dis- tillation, From the perspective of effectiveness, we use data augmentation techniques to com-
head n. /_head_
Per-Structure Pearson
0.55
0.50
head
15.45%
speed up x87,9
0.45
MIll Nir-Neework
RDE Network
DiNAriity
pensate for the weakening of modeling capabil-
ProMMM
0.40
Prompt-DDG
ity brought by architectural simplification. To achieve this, we collected, annotated, and re- leased a large-scale augmented dataset contain- ing millions of mutation data for pre-training Light-DDG. A comparison of various AAG prediction methods on the effectiveness and ef- ficiency is presented in Fig. 1. It demonstrates the great advantages of Light-DDG, e.g., 89.7
Light
10-2
10-1
10a
10a
AAG Prediction Speed (s/sample)
Figure I: Efficiency vs Effectiveness. There are three variants of Light-DDG with differing num- bers of attention heads (default to 4 in this paper).
inference acceleration and 15.45% performance gains over Prompt-DDG. Furthermore, we compre- hensively evaluate the advantages of Light-DDG in terms of test generalizability, noise robustness, architectural applicability, and inference practicality by extensive experiments in Sec. 5.
Furthermore, we show that even simple yet effective Light-DDG has the potential to be good explainer and optimizer within a Unified framework for Antibody optimization (Uni-Anti). For the target antibody, we propose a Mutation Explainer to identify key mutation sites and learn site-wise mutation preferences. One of the design difficulties is the synergistic effect of mutations, e.g., the negative effect of single substitution can only be tolerated in the presence of another enabling mutation (Ding et al., 2024). To tackle this problem, we develop an iterative Shapley value estima- tion algorithm that can measure the marginal benefit of each mutation per residue by coarse-to-fine iteration, while reducing the huge combinatorial space of vanilla Shapley value algorithm (Shapley al., 1953). Based on the learned mutation preferences, we explore accessible evolutionary regions by mutation prefcrence-guidee antibody optimization and then evaluate antibody candidates quickly using Ligh-DDG. Such antibody optimization enjoys the great benefits of diversity and flexibility, capable of generating diverse antibodies with corresponding AAG scores and rankings, and is well suited for co-optimization of multiple CDRs. Finally, we demonstrate the advantages of Uni-Anti for antibody optimization and preference explanation using case study on SARS-CoV-2.
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Published as a conference paper at ICLR 2025
2 RELATED WORK
Mutational Effect Prediction. The prediction of mutation effects single proteins has been well studied, which mainly mines co-evolutionary information from protein sequences by Multiple Se- quence Alignments (MSAs) (Frazer al., 2021; Luo et al., 2021) or Protein Language Models (PLMs) (Meier et al., 2021; Notin et al., 2022). However, predicting the change in binding free energy (AAG) of protein complexes upon mutations is more challenging because involves com- plex interactions between proteins. Computational methods for A&G prediction have undergone a paradigm shift from biophysics- based and statistics- techniques (Schymkowitz et al., 2005; Park et al., 2016) to Deep Learning (DL) techniques, among which pre-training- are the most popular solutions. RDE (Luo et al., 2023) pre-trains by using a normalizing flow model to estimate the density of sidechain conformations (rotamers). Similarly, DiffAffinity (Liu et al., 2023) also models the side-chain distribution, but with a conditional diffusion model. Be- sides, Mo et al. (2024) proposes multi-level pre-training framework, ProMIM, to fully capture all three levels of protein-proted interactions. Recently, Prompt-DDG (Wu et al., 2024c) proposes a microenvironment-awwanr aware hierarchical codebook that generates prompts for better AAG prediction.
approaches
Antibody Optimization. Early approaches for antibody design are mostly energy-based (Adolf-
Bryfogle et al., 2018; Lapidoth et al., 2015), and it is recently extended to deep generative models, including RefineGNN (Jin et al., 2021), MEAN (Kong et al., 2022), RAAD (Wu et al., 2024a), DiffAb (Luo et al., 2022), etc. These models train a conditional antibody generator and screen out a number of high-quality antibodies using a AAG predictor. These high-quality antibodies will be used as training data to further fine-tune the antibody generator for directed antibody optimization. In this paper, we rethink the role of AAG prediction for antibody optimization, demonstrating that a simple yet effective AAG predictor can directly serve as good unsupervised antibody optimizer and explainer, without requiring additional functional annotations or deep generative models.
PRELIMINARY
Notations. A protein complex consists of N amino acid residues (V1, U2. ,N)), where each residue & is one of the 20 amino acid types. We characterize each residue is with E(3)- invariant node feature x; = Exype (1s), Fany(02) (n),, Emm (U:)}, where Exype (1x) denotes the embedding of residue types, Eumgle (1;) (v:)) is the angle encoding of three dihedral angles and four torsion angles, and Eman (x)) denotes the mutation embedding on whether residue v; is mutated or not. The pairwise
Z, (). where feature between residues es and uy is eij (o, Edis (Ze Zz), Q, [Z,,c
Z, is the 3D coordinate of residue ut, jj and Eais (Z,, Z,) encode the relative sequential and spatial distances between residue 2/ and residue Vy. respectively. (i. is set 0 for any two residues that are not on the same chain. Besides, the last term the direction encoding of four back- bone atoms Ç (Ca, C, N, 0) of residue vy in the local coordinate frame Qi of residue vv (Wu et al., 2024c). All these node and pairwise features will be pre-processed before model training.
Transformer as the student backbone in Knowledge Distillation (KD). To improve the inference efficiency of a A AAG predictor, a lightweight Transformer is used as the backbone to encode each protein complex P E). The 1-th < L) layer of the Transformer is defined follows
= LN FFN (headl, heaax w!)) + H((-))
where
(H (H- K
(1.))
heads- softmax
(W H w(!)) (1.)
(LA)
Vdn
(b.)) E are parameter matrices, KJ is the number of attention heads, LN()) is the layer normalization, FFN(-) is two-layer feed-forward neural network with ReLu(-) as activation function, and d, is the hidden dimension.
where (0) X denote the input node feature, (! ((lk1) (h.))
(k.))
K
Prompt-DDG as Teacher and Annotator. Prompt-DDG (Wu et al., 2024c) is the otate-of-the-art AAG predictor to date. During training. it trains a hierarchical prompt codebook to capture mi- croenvironmental information different structural scales. With the learned prompt codebook, it encodes the microenvironment around each mutation into multiple hicrarchical prompts and com- bines them to flexibly provide information wild-type and mutated protein complexes about their
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microenvironmentall differences. We use Prompt-DDG teacher and annotator for distillation and data augmentation in default in this paper, but also evaluate other AAG predictors as teachers.
Problem Statement. Given a wild-type protein complex Pw and set of mutations M, the task of mutational effect prediction aims to learn a mapping f()) : . + AAG that encodes wild-type complex Pw and mutated complex PM g(W, M) separately with a parameter-shared Trans- former, and then feeds the difference of their pooled representations h W and ha into a three-layer MLP to predict the AAG score. The objective of protein (antibody) complex optimization aims to find a mutation S from the mutation space S that minimizes AAG, that is, arg minges [(Pw,
4 METHODOLOGY
In this section, we propose a unified framework for directed antibody optimization with simple but effective AAG predictor as the core. A high-level overview of the proposed framework is shown in Fig. 3(b). We first present how to construct a large-scale augmented mutation dataset SKEMPI- Aug by cross-augmentation in Sec. 4.1. Next, we pre-train a simple but effective Light-DDG on the large-scale augmented SKEMPI-Aug dataset and then fine-tune it by knowledge distillation on the SKEMPI v2.0 dataset, as described in Sec. 4.2. Further, we propose a Mutation Explainer to learn key mutation sites and mutation preferences in Sec. 4.3, and finally introduce how to perform preference-guided mutation search in Sec. 4.4. From the perspective of the energy landscape in Fig, 3(a), Light-DDG establishes mapping from mutations to energy changes (AAG), while Mu- tation Explainer iteratively explores evolutionary accessible regions based on mutation preferences.
4.1 LARGE-SCALE AUGMENTED MUTATION DATASET FOR SUPERVISED PRE-TRAINING
Considering the scarcity of experimental data in the SKEMPI v2.0 dataset, pre-training on large amounts of mutations-irrelevant data has become a popular practice for training AAG predictors. One of the most commonly used pre-training datasets is PDB- REDO (Joosten et al., 2014), in which several unsu- pervised pre-training tasks (Luo et al., 2023; Yang et al., 2022; Mo et al., 2024), have been proposed to learn generalized knowledge. However, in order to improve the inference efficiency, transformer as the backbone in this paper, which has weaker modeling capability than the IPA-style back- bone, making it hard to directly learn useful knowl- edge patterns for AAG prediction from massive unla- beled data in unsupervised manner. Therefore, we here consider supervised pre-training, but the upcom- ing challenge is how to construct a large-scale dataset that covers a sufficiently wide range of mutation pos- sibilities and their corresponding AAG scores. In this subsection, we take data augmentation as an effective means of compensating for the simplification of the chitecture. To augment data, we use Prompt-DDG, the current state-of-the-art AAG predictor, as an annotator. Specifically, we perform arbitrary mutations several randomly selected mutation sites of complexes from the SKEMPI v2.0 dataset, feed the mutated complexes into Prompt-DDG to predict AAG scores, and then package the mutations and predicted AAGs into one piece of augmentation data. To prevent data leakage, we propose K-fold coss-auggmnnttionn as shown in Fig. 2, where the SKEMPI v2.0 dataset is divided into K equal-sized folds according to the com- plex structure. For each round of augmentation, we first train a new Prompt-DDG from scratch with K-1 folds, then augment the remaining fold by random sampling and random mutation, and finally annotate it by Prompt-DDG. As a result, the data used to train Prompt-DDG is separate from the data annotated by Prompt-DDG to avoid any possible data leakage. Moreover, we set a thresh- old during augmentation to ensure that the augmented samples original samples to further avoid data leakage. In such a way, we have augmented, annotated, and released large-scale dataset called SKEMPI-Aug, which contains millions of mutation data that
Table 1: Characteristics of three datasets.
Dataset
Size
Mutation
Pre-training
SKEMPI v2.0
7k
X
PDB-REDO
143k
X
unsupervised supervised
SKEMPI-Aug
640k
Foul
Fold
Fold
Fold
a lightweight
Fold
Fold
Fold
Fold
S2: Augmentation
S1: Treining
Mutation Data
83: Prediction
Promot-DDG
Figure 2: A schematic diagram of the K- fold cross-augmentation, where blue and red boxes indicate the separate folds used for training and data augmentation.
sufficiently different from the
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Unitted Framework for Anilbody Optimization (Uni-Ant)
Trobi Corple.
Eaxr
Mustion Boarch
Vunder
DOG Prodecs
gru g0400))
Pre-triniing
Model
Fine-tuning
Muddion Din
Seep bass
gN+00G)
Mintton
+
19
Vuzise Prefennco
Exstretoore
Scores Rarkings
(a) Energy Landscape
(b) A High-level Overview of Uni-Anti with Light-DDG as the core.
Figure 3: (a) A binding energy landscape reflecting the mapping from mutations to AAG scores, (b) Pre-training Light-DDG with augmentation and distillation, and then using it as the core, together with mutation explainer and search, to construct a unified framework for antibody optimization.
can be used for supervised pre- pre-training of AAG predictors. We compare Table. the data sizes of three datasets, whether they include labeled mutation data, and how they used for pre- pre-training.
4.2 SIMPLE BUT EFFECTIVE AAG PREDICTOR BY AUGMENTATION AND DISTILLATION
Knowledge Distillation (KD) is an effective means of achieving model compression (Hinton al., 2015), and the distilled student models can even exhibit better performance than the corresponding teacher models. this paper, we combine knowledge distillation techniques with supervised pre- training to build simple but effective AAG predictor (Light-DDG). Specifically, we first perform supervised pre-training on the large-scale SKEMPI-Aug dataset Dugg and then fine-tune the model on the SKEMPI v2.0 dataset Dskem under the joint supervision of ground-truth labels and distillation losses. Since cross-vale vols-vvaldation on SKEMPI v2.0 is performed this paper to validate the method, the distillation objective of Light-DDG on the training data Durin S Dskem can be defined as:
f3 arg min f's [Drain
Σ
[1's(a:)) - vell +B +1(((((a))) -M(a)))))))))
(2)
EaaG
7xn
where 3 is a trade-off hyperparameter, ground-truth AAG. In this paper, we default to Prompt-DDG the teacher (+). but also ob- served significant improvements when using other AAG predictors as teachers in the experiments. The student model f-(-) is initialized by pre- pre-training on the SKEMPI-Aug dataset DAug. follows
(P)) is the input to AAG predictor f(.), y; is the
I's = arg min Is (Aug.
Σ
[ss(a)) will
Casc
4.3 MUTATION EXPLAINER: LEARNING MUTATION SITES AND MUTATION PREFERENCES
A key challenge in antibody optimization is how multiple mutations combine to influence function and future mutation trajectories (Ding al., 2024). With simple but effective Light- DDG available, we propose novel Mutation Explainer that can identify key mutation sites and learn mutation preferences for each residue site. This is achieved by calculating the Shapley value (Shapley al., 1953) for each mutation at each site as its marginal benefit, which explains very well the "average" marginal contribution of each mutation across all mutation combinations. The exact Shapley value (ii,j) of the i-th (1 < i < N) site mutated to j-th (1 < < 20) amino acids is defined as follows
SSI(MM S MI!
1)!
p(ij)=
Σ
(4)
MC
The exact Shapley value (pii is calculated by considering the effects AAG scores when each mutation (i.j) is added or removed from the mutation set M. However, is impractical to enumer- ate all mutation possibilities in the huge mutation space $ to calculate exact Shapley value (iii,))
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For the task of antibody optimization, what we are really concerned about are those promising mu- tations rather than those unimportant or even harmful ones. Therefore, we propose a more efficient Iterative Shapley Value Estimation algorithm, which estimates the Shapley value of cach mutation in a coarse-to-fine iterative manner, and progressively pays more attention to those promising residue sites and mutations. Specifically, the Shapley value (i, at t-th iteration estimated as follows
D (1.)) Σ D
1stsT
(5)
where ).M are the numbers of sampling for each residue site, proportional the site proba- bility (t) E R. MM, is one mutation set that randomly selects multiple residue sites except for the
i-th residue and mutates them according to the mutation preference Pure E R R.Nx20 Specifically, the
site probability Psite
((+1) and mutation preference for the i-th site Ppre (i)
((+1) (i)) is updated follows
(t+11) a..o Σ क (1.5) + (1 a) ple Psle
Ppne ( ...
(0) :)) + (1 -a) - (6)
(1) where Paite and {prr (i) are initialized to be uniform distributions, is the momentum updating rate, and (.) = Softmax(() is the activation function. Such an iterative approximation will treat ev- ery site and mutation equally at first, but gradually focus on those more potential sites and mutations to approximate the exact Shapley values as closely as possible in a limited number of samples.
(1)
4.4 PREFERENCE-GUIDEE MUTATION SEARCH FOR ANTIBODY OPTIMIZATION
Mutation and selection are two fundamental aspects of antibody optimization. The previous popular methods usually train a deep generative model on large amounts of data, and then apply Iterative Target Augmentation (ITA) to guide directed optimization, i.e., generating favorable mutations. In contrast, this paper focuses on selection rather than generation. Given a lightweight Light-DDG and a Mutation Explainer, we can directly search for favorable mutations, requiring no additional deep generative models. For the target antibody to be optimized, we randomized 10,000 mutated an- tibodies by sampling mutation sites and determining mutation residues based on the site importance (T))
Pate and site-wise preferences {prre (i) These mutation candidates are then quickly evaluated using a lightweight Light-DDG to get the most desirable mutations based on the rankings of their AAG scores. We have provided pseudo-code in Appendix A about how Light-DDG, Mutation Explainer, and Mutation Search are constructed into a unified framework for antibody optimization.
EXPERIMENTS
Datasets. The effectiveness of Light-DDG for AAG prediction is evaluated the SKEMPI v2.0 (Jankauskaité et al., 2019) dataset, which contains 348 complexes, 7,085 mutation combina- tions, and corresponding changes in binding free energy, but not any mutated complex structures. We randomly split the SKEMPI v2.0 dataset into 3 folds by complexes and perform 3-fold cross- validation for all methods. For pre-training, different pre-training--ase
methods use different pre- text tasks and datasets. For example. the REB-REDO (Joosten et al., 2014) dataset contains 143k unannotated data and has been widely used for unsupervised pre-training by previous methods. In contrast, Light-DDG is supervised pre-traive pre-trdieed on the SKEMPI- notated mutation data. Moreover, the AFDB dataset (Varadi et al., 2022) that contains the sequences and their corresponding structures predicted by AlphaFold2 can also be used as pre-training data.
datasets that consist of 670k an-
Evaluation Metrics. There are seven metrics used to evaluate AAG prediction, including five overall metrics: (1) Pearson correlation coefficient; (2) Spearman correlation coefficient; (3) Root Mean Squared Error (RMSE); (4) Mean Absolute Error (MAE); (5) AUROC. Additionally, (Luo et al., 2023) groups the mutations by structure, calculating the Pearson and Spearman correlation coefficients for each structure separately, and reporting the average as two per-structure metrics. For antibody optimization, take the minimal AAG score of the optimized antibodies as the metric.
Baselines. We compare Light-DDG with several state-of-the the--rr--rt AAG prediction methods, includ- ing ES (Hsu et al., 2022), two variants of MIF Alogits and MIF-Network) (Yang al.,

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Table 2: Mean results of 3-fold cross-validation for AAG prediction on the SKEMPI v2.0 dataset.
Pre-training Datasel (Szie)
Per-Structure
Overall
Category
Method
Pearson 1
Spear
Pearson
Spear.
RMSE
MAE 1
AUROC
ESM-1v
0,0073
-0,0118
0.1921
0.1572
1,9699
1.3683
0.5414
Soquence-bssed
PSSM
0.0826
0.0822
0.0159
0.0666
1.9978
1. 138855
0.5260
MSA Trans.
0,1031
0.0868
0.1173
0.1313
1.9835
1.3816
0.5768
Traneptiion
0.1348
0. 1236
0.1141
0.1402
2,0382
1,3883
0.5885
Energy Function
Rosetta
0,3284
0.2988
0.3113
0.3468
1.6173
1.1311
0.6562
FoldX
0.3789
0.3693
0.3120
0.4071
1,9080
1 L,3089
0.6582
Supervised
DDGPred
0,3750
0.3407
0.6580
0.4687
1.4988
1.0821
0.6992
End-to-End End
0,3873
0.3587
0.6373
0.4882
1.6198
1.1761
0,7172
AFDB
ESM-1F
0.2241
0.2019
0.3194
0.2806
1.2857
0.5899
B-factor
0,2042
0.1686
0,2390
0.2625
2.0411
1.4402
0.6044
Alogit
0.1585
0.1166
0.2918
0.2192
1.9012
1.3301
0.5749
Pre-training
MII--Netork
0.3965
0.3509
0.6523
0.5134
1.5932
1.1469
0.7329
PDB-REDO
RDE-Linear
0.2903
0.2632
0,4185
0.3514
1,7832
1.2159
0.6059
NDE-Network
0.4448
0.4010
0.6447
0.5584
1.5799
1.1123
0.7454
DiflAlinity
0.4220
0.3970
0.6690
0.5560
1.5350
1,0930
0.7440
ProMIN
0.46-10
0.4310
0.6720
0.5730
1.5160
1,0890
0,7600
SKEMPI v2.0
Prompt- DDG
0.4712
0.4257
0.6772
0.5910
1.5207
1,7700
0.7568
Ours
SKEMPI Aug.
Amorp-tomss
Light-DDG
+15,45%
0.5440
+17.55%
0.5004
+9.70%
0,7429
+14.50%
0.6767
+9.01%
1.3837
+9,9668
0.9697
+4.85%
0.7935
Pearson: 0.7429
Per Structure Pearson
Per Structure Pearson
0180
Prodictod AdG
Spearan:: 2.6767
0.6.
2001
10444
0.4
12:30
-1.0
-05
0.448
0,2200
0.4640
D/4712
0.5440
0.0
0.2
-5
10
M0 Network
RDE-Newook bittationty
Prodiit
Prompss DDG
MM Wi-Neteerf:
HDE-- litioonnit Protis
Prompt Dog
Expermental A&G
(a) Correlation Analysis
(b) Score Distribution
(c) Eight Complex Examples
Figure 4: (a) Correlations between experimental and predicted AAGs. (b) Distributions of per- structure Pearson scores. (c) Per-structure Pearson correlation scores for eight complex examples.
2020a), two variants of RDE (RDE-Linear and RDE-Network) (Luo et al., 2023), DiffAffinity (Liu et al., 2023), ProMIM (Mo et al., 2024), Prompt-DDG (Wu et al., 2024c), and model that is pre- trained to predict the B-factor of residues and use predicted B-factors to predict AAG. Moreover, we compare the performance of Uni-Anti for directed antibody optimization with RefineGNN (Jin et al., 2021), MEAN (Kong et al., 2022), DiffAb (Luo et al., 2022), and dyMEAN (Kong et al., 2023). The detailed hyperparameter and implementation details can be found in Appendix B.
5.1 EVALUATION ON AAG PREDICTION
Performance Comparison. Table. 2 reports evaluation metrics for 18 methods on the SKEMPI v2.0 dataset, from which we observe that Light-DDG significantly outperforms all baselines on all 7 metrics, especially on the two critical per-structure metrics. For example, Light-DDG improves over Prompt-DDG on per-structure Pearson and Spearman by 15.45% and 17.55%, respectively.
Visualizations. The scatter plots of experimental AAG and predicted AAG for Light-DDG, pre- sented in Fig. 4(a), demonstrate the strong correlation between experimental and predicted results. Besides. we provide the distribution of per-structure Pearson scores in Fig. 4(b), as well as the av- erage results across all structures. Not only does Light-DDG achieve the best average performance, but its distribution is mostly centered on high correlation, with fewer low-correlation structures. Further, randomly select 8 complexes and present their per-structure Pearson scores in Fig. 4(c), where Light-DDG achieves the best performance on of 8 complexes.
Single and Multiple Mutations. We further compare Light-DDG with 7 superior methods from Table. 2 under single- and multi-point mutations, respectively. The results in Table. 3 show that two state-of-the thett---tt-ttttt methods, Prompt-DDG and ProMIM, each have strengths in different metries and mutation settings. However, Light-DDG significantly outperforms all baselines by a large margin on 14 metrics in both mutation settings, especially more challenging multi-point mutations.
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Table 3: Performance comparison of AAG prediction under single-point and multi point mutation.
Pre-training Dutaset (Szie)
Per-Structure
Overall RMSE
Method
Mutations
Pearson
Spear.
Pcarson
Spear.
MAE
AUROC1 f
DDGPred
x
single multiple
0.3711
0.3427
0.6515
0,4390
1,3285
0.9618
0,6858
0.3896
0.5938
2.1813
0.7590
End-to End
x
single
0.3818
0.3426
0.6605
0.4594
1.3148
0,9569
0.7019
multiple
0.4178
0.4034
0.5858
0.4942
2.1971
1.7087
0,7532
MIF-Network
PDB-REDO
single
0.3952
0.3479
0.6667
0,4802
1.3052
0.7175
(143k)
multiple
0,3968
0,3789
0.6139
0,5370
2,1399
1.6422
0,7735
PDB-REDO (143k)
single
0.4687
0.4333
0.6421
0,5271
1.3333
0.9392
0.7367
RDE-Network
multiple
0.4233
0.3926
0.6288
2,0980
0.7749
DiflAffinity
PDB-REDO (143k)
single
0,4290
0.4090
0.6720
0.5230
1 1.2880
0.9230
0.7330
multiple
0.4140
0.3870
0.6500
0.6020
2.0510
1,5400
0,7840
ProMIM
EDB-REDO
single
0.4660
0,4390
0.6680
0,5340
1,2790
0,7380
(143k)
multiple
0.4580
0.4250
0.6660
0,6140
1.96.0
1,4910
0.8250
Prompt-DDG
SKEMPI v2.0 (7k)
single
0.4736
0.4392
0.6596
0,5450
1.3072
0.9191
0.7355
multiple
0.4448
0.3961
0.6780
0.6433
1.9831
1.4837
0.8187
Light-DDG
SKEMPI Aug.
single
0.5505
0.5114
0.7328
0.6384
1.1835
0.8245
0.7777
(670k)
multiple
0.5146
0.4764
0.7467
0.7343
1.7948
1.3431
0.8504
Table 4: Ablation study on knowledge distillation, data augmentation, and different input contexts.
Method
Component
Per-Structure
Overall
K1)
Augment.
Wild Str.
Mutant Str.
Pearson 1
Spear.
Penrson
Spear:
RMSE
MAE
AUROC:
Promp-DDG
x
X
0,4712
0.4257
0.6772
0.5910
1,5207
1,0700
0.7568
x
x
V
X
0,3888
0.3576
0.6142
0.5244
1,6310
1,1622
0,7209
/
x
1
X
0.6297
1.0177
Light-DDG
x
X
0,4516
0,4087
0.6754
0.5796
1,5242
1,0994
0.7531
/
X
0.5440
0,5004
0.7429
0.6767
1,3837
0.9697
0.7935
x
X
0.4154
0,3749
0.6842
0.5590
1,5631
1.1166
0.7345
0.5496
0,5052
0.7482
0.6824
1.3807
0.9621
0,7968
StrSStrutture Pearson
0.60
Studant (uail)))
Sueee KD0
(erila) Becwe Oe Aug:
Per Strustuuuure Pearson
0.55
Aug
cosy
Structure Pearson
0.5
5:524
0.55
2,52
0.50-
Eato
0.4
4en
0 3.355
0.50
0.45
0.3
RDC-heieare
0.222
0.234
8327
0.40
01
0.2
Proman-366
0.27
0.40
1.30
Lipa- 83G teauunce-co//
0.35
16
32
64
0.1 0.5
3.0 4.0 5,0 6.0
RDE-PPI
DiffAfinity Prompt-DDG
Augmentation Data (x10*)
Structural Noise Variance
(a) Architectural Applicability
(b) Augmentation Sensitivity
(c) Noise Robustness
Figure 5: (a) Applicability of using different AAG predictors teachers. (b) Sensitivity the sizes of augmentation data. (c) Robustness of different AAG predictors to 3D structure Gaussian noise.
Ablation Study. We conduct an ablation study to evaluate the roles played by KD and augmentation. can be observed from 'able. 4 that (1) both KD and augmentation help to improve performance alone, even over Prompt-DDG (as teacher for KD and annotator for augmentation); and (2) combin- ing the two further brings performance gains on top of each other. Furthermore, we consider two alternative input contexts, including (i) only wild-type and mutated sequences are available, and (ii) both wild-type and mutated structures are provided, where we predict mutated structures from mu- tated sequences by ESMFold (Lin al., 2022). It can be found that (1) even sequence-only design performs better than energy-based based and supervised baselines in Table. 2, but poorer than structure- based design, which demonstrates the importance of structural information for AAG prediction. (2) Mutated structures only slightly improve the performance, Prompt-DDG (teacher model) has already been implicitly pre-trained to be mutated structure-aware.
Applicability, Sensitivity, and Robustness. We evaluate the applicability of Light-DDG to dif- ferent teachers Fig. 5(a), where the distilled students perform better than corresponding teachers across various architectures. More importantly, it significantly improves performance regardless of whether teachers or students are pre- pre-trained on the SKEMPi-Aug dataset. Moreover, we evaluate how the sizes of augmentation data influence Light-DDDG under w/ and w/o KD settings, respec-
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tively. The curves in Fig. 5(b) exhibit consistent improvements from more augmented data; however, the performance gain becomes gradually limited as the data becomes more extensive. Furthermore, evaluate the robustness of Light- DDD to 3D structural noise by adding Gaussian noise with dif- ferent variances to the wild-type structures in the inference phase. It can be found from Fig. 5(c) that the performance gets poorer with larger noise, even poorer than that of sequence-only Light-DDG. Considering that the errors of existing structure prediction are mostly around IA, in this case only structure-based Light-DDG achieves better performance than sequence-only Light-DDG.
Table 5: Rankings of the five favorable mutations on Table 6: Average AAG (kcal/mol) after anti- body optimization targeted SARS-CoV-2.
the antibody screening against SARS-CoV-2
Method
THEIW
AH53F
NH57L
RHIUSM
LH104F
Avg. Rank
Method
CDR-HI
CDR-H2
CDR.H3
CDR-H1/23
Best
Rosetta
10.73%
76.72%
53.93%
11.34%
27.92%
6.40
7
6 Sites
13 Sites
26 Sites
FolEX
DIKGPred
68.22%
13.56%
2.63%
6.88%
12.35%
5.67%
16,80%
8.30%
66.19%
4,60
5,80
MEAN
RefineGNN
-0.644
00473
-1.653
-1.310
40642
00.88
-1.653
-1310
MIK-Net.
24.49%
4.05%
6.48%
30.3556
36,2346
6.60
DifAAb
-1.826
.0.56
-1.828
RDF--Na
30.65%
61.54%
3.40
dyMEAN
-0.899
-1.942
0.735
DidlAfiiity
7.28%
3.64%
18.82%
81.78%
Random
-1.865
-1.325
Prompt-DDXi
2022%
6.88%
3.24
34.81%
0.48%
4.00
CMA-ES
SE-dWIS
-0.972
-1124
-1.910
-1.957
.0683
0.770
-1.975
2.259
-1.975
-2225
Uni-Anti (curs)
1.21%
2.23%
74.90%
5.289
2,40
Directed
-1.241
-2.192
40946
.2.72
-2872
5.2 ANTIBODY SCREENING AND OPTIMIZATION AGAINST SARS-COV-2
Candidate Antibody Screening. The inference-efffcient property of Light-DDG makes it well- suited for candidate antibody screening, i.e., identifying desirable mutations from a pool of potential mutations. We take the computational screening of 494 candidate human antibodies against SARS- CoV-2 as a case study, where all mutations are located at 26 sites within three CDRs of the heavy chain. We predict AAG's for all candidate antibodies, rank them in ascending order (lowest AAG in the top), and report in Table. the ranking of five favorable mutations that have been previously proven effective (Shan et al., 2022; Wu et al., 2024c). It can be seen that (1) Uni-Anti ranks first on two antibodies and second on the other two; (2) only Uni-Anti successfully identifies three mutations with rankings smaller than 5%; (3) Unit-Anti has the best average ranking of 2.4 among 9 methods.
Antibody Optimization against SARS-CoV-2. We show the effectiveness of Uni-Anti in opti- mizing human anti-SARS-Cov-2 antibody to produce variants with lower binding energy. We first compare directed mutations (based on explainable mutation preferences) with random muta- tions in Table. 6, where Light-DDG in less than 5 minutes) and filter out the best one. It is evident that directed muta- tions perform better than random mutations, especially on multi-site mutations. For example, joint random mutation of three CDRs is surprisingly inferior to mutating only CDR-H2, but directed mutation benefits remarkably from a wider range of mutation sites. Further, we compare several generative antibody optimization methods, including RefineGNN (Jin et al., 2021), MEAN (Kong al., 2022), DiffAb (Luo al., 2022), and dyMean (Kong et al., 2023). We input their generated antibodies together with wild-type complex structures into Light-DDG to predict AAGs. Note that these methods are conditional generative models focusing on the generation of a single CDR region, and cannot handle the joint optimization of multiple CDRs with official pre-trained models. It can be seen that regardless of which CDR region is optimized, Uni-Anti has significant advantage over other basclines. Besides, joint optimization of three CDR regions leads to larger performance gains.
evaluate the AAGs of 10,000 sampled candidate antibodies (done with
Furthe, we take Light-DDC as the fitness function and further consider two additional search strate- gies, guidedd dWJS (gg-dWIS) (Ikram et al.) and CMA-ES-based (Claussen et al., 2022). can be observed that (1) when the mutation space is relatively large, probably because the multivariate normal distribution in CMA-ES is not a reasonable prior for antibody mutations. (2) When optimizing a single CDR with a small mutation space, gg-dWJS slightly outperforms the current SOTA generative model, dyMEAN. However, gg-dWJS cannot benefit from such a large mutation space as Uni-Anti when jointly opti- mizing multiple CDRs. Last but not least, the implementation of these two approaches relies on the efficiency of Light-DDG. More results on antibody optimization can be found in Appendix C.
approach has an advantage over random mutation only
5.3 VISUALIZATIONS ON EXPLAINABLE MUTATION PREFERENCES
Single and Pairwise Mutations. We demonstrate how Mutation Explainer can explain and guide antibody optimization, with the anti-SARS-Cov-2 antibody example. We present AAGs of

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Published as a conference paper at ICLR 2025
COA Rootton
CDI
-04
00
-22
3x
ACDUT an
Residas Type
CDR Positin
Residue Type
(a) Single Mutation
(b) Pairwise Mutations
(c) Mutation Preferences
(d) Evolutionary Tree
Figure 6: Visualizations on the optimization of the three CDR regions the heavy chain for an anti- SARS-CoV-2 antibody. (a) AAGs for single mutation. (b) Average AAGs for pairwise mutations. (c) Explainable mutation preferences based on the estimated Shapley values. (d) An example of the mutation evolutionary tree (only part of the mutations presented for clear visualizations).
single mutation and average AAGs of paired mutations in the three CDRs of the heavy chain, respectively, It can be seen that Mutation Explainer well identifies five valid mutations (marked in black box) that have been proven effective by previous literature (Shan et al., 2022). Besides, it is clear that mutating CDR-H2 can usually lead to smaller AAGs than mutating CDR-H3 in Fig. 6(a). Moreover, pairwise mutations in Fig. 6(b) reveal important synergistic effects of mutations, i.e., a single mutation that works well may fail when occurring with other mutations (Ding et al., 2024).
Mutation Preferences. Considering that multiple mutations are a common application scenario, Shapley values of AAG scores used to estimate the marginal benefits of individual mutations, as shown in Fig. 6(c). For example, the 55-th residue on the heavy chain usually results in a negative gain when mutated alone in Fig. 6(a), but it has a small Shapley value in Fig. 6(c), suggesting its important role for multiple mutations, i.e., that it may need to work together with other mutations.
Mutation Evolutionary Tree. Using lightweight AAG predictor, along with the learned mutation preferences, we can draw mutation evolutionary tree for the target antibody. as shown in Fig. 6(d), which is expected to provide some insights, explanations, and guidance for antibody optimization.
CONCLUSION AND DISCUSSION
This paper shifts the research focus from generating mutations evaluating mutational effects and indirectly explores the underlying fitness landscape by focusing on regions where AAGs are min- imized. To this end, we train simple but effective AAG predictor (Light-DIG) by data augmen- tation and distillation. Furthermore, we show that Light-DDG can serve as a good optimizer and explainer within a Unified framework for Antibody optimization (Uni-Anti). Extensive experiments show the superiority of Uni-Anti in mutational effect prediction, optimization, and explanation.
Broader Impact. The huge combinatorial space of potential mutations and the scarcity of mutation annotations have long been considered two obstacles straddling the path to unsupervised protein evolution. On the data side, the released augmented mutation dataset expands the original data by two orders of magnitude and is expected be a solid data ground for follow-up works. On the methodology side, a lightweight AAG predictor is expected to facilitate high-throughput fast mutation screening. In addition, the mutation preference explanations learned by Mutation Explainer can reveal the potential evolutionary paths, providing a powerful guideline for the understanding of protein functions and the discovery of high-fitness variants. Last but not least, mutation and selection are the two pillars of natural evolution. This paper provides new perspective to achieve a novel, explainable, and unsupervised framework for directed optimization with selection its core.
Limitations. Despite the great progress, several limitations still remain. Firstly, AAG is only one common prior that constrains the evolution of proteins; combining other priors can still be built on top of our framework. Secondly, distillation is only one of the strategies to achieve lightweight in- ference, and other architectural choices, quantization, sparsification, and parallelization are also op- tional from an engineering perspective. Thirdly, the design of this paper is expected to be combined with deep generative models. We believe that (1) constructing preference pairs using Light-DDO for preference alignment and (2) taking Light-DDG generation are two promising solutions. Finally, more case studies on other proteins (in addition to antibodies) and wet experimental assays of the optimized antibodies will be left for future work.
guidance in diffusion models for controllable
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7 ACKNOWLEDGMENTS
Many thanks to Siqi Ma for his contribution to the platform development. This work is supported by National Science and Technology Major Project (No. 30222D0115101), National Natural Science Foundation of China Project (No. 624B2115, No. U21A20427), Project (No. WU2022A009) from the Center of Synthetic Biology and Integrated Bioengineering of Westlake University, and Project (No. WU2023C019) from the Westlake University Industries of the Future Research Funding,
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(1) Published as a conference paper at ICLR 2025
A. PSEUDO CODE
The pseudo-code of the proposed Uni-Anti framework is summarized in Algorithm 1.
Algorithm A Unified Framework for Antibody Optimization (Uni-Anti)
Input: M Wild-type Complexes and Mutations (() f==1- 1: Randomly initializing the parameters of the student model fs(()-
2: # Augmentation Pre-training
3: Pre-training the student model fs(-) on the augmented SKEMPT-Aug dataset by Eq. (3).
4: # Training AAG Predictor (Light-DDG)
5: Encoding the input data with the teacher (-) and the pre-trained student [s(-) separately;
6: Calculating the knowledge distillation (KD) loss;
7: Fine-tuning the student fs(-) by joint optimization downstream and KD losses by Eq. (2).
8: # Mutation Explainer
9: Initializing Psite and (Pire (z) uniform distributions.
(1)
for E (1, 2, .., T) do
11: Calculating the shape value of each mutation each site by Eq. (5);
Updating the site importance Psice (t++) and mutation preferences (pple (1) by Eq. (6).
13: end for
14: # Directed Mutation Search
15: Randomly sampling 10,000 mutation candidates based on (T) and {ppe (i) hi)
16: Predicting and ranking AAG scores of sampled mutations using the trained Light-DDG;
17: Screening out the most desirable mutations based on the rankings of their AAG scores.
18: return Trained AAG predictor (Light-DDG) and optimized antibodies.
B. HYPERPARAMETERS AND IMPLEMENTATION DETAILS
Experiments are conducted based on Pytorch 1.8.0 on hardware platform with Intel(R) Xeon(R) Gold 6240R 2.40GHz CPU and NVIDIA A100 GPU. The hyperparameters are as follows: learn- ing rate Ir = 0.0003, batch size B = 32, pre-training iterations EAUg = 5, 000, AAG iterations EAAG 15, 000. hidden dimension F 128, number of Transformer layers
4, number of
attention heads K 4 (by default), loss weight B = 0.1, and momentum rate a = 0.9. Besides, we crop sequences or structures into patches containing 32 residues by first choosing seed residue, and then selecting its 31 nearest neighbors based on the sequential distances the C3-Cp distances.
C. ANTIBODY OPTIMIZATION ON SABDAB
To further demonstrate Uni-Anti's effectiveness in antibody optimization addition to anti-SARS- CoV-2 antibody, we further optimize CDR-H3 of 500 antigen-antibody complexes from the SAbDab dataset (Dunbar et al., 2014) and report the average (optimal) AAG scores of various basclines in Table. A1. For RefineGNN, MEAN, dyMEAN, we incorporate Iterative Target Augmentation (ITA) (Yang et al., 2020b) into the optimization process to fine-tune the generators. For DiffAb, we directly generated 10,000 candidate samples and then selected the best one. For all baselines, we feed their optimized sequence together with wild-type complex structures into Light-DDG predict AAGs. The results in Table. Al show that Uni-Anth achieves superior results with a notably lower average AAG score, demonstrating its potential advantages in terms of antibody optimization.
Table A1: Average AAGs after optimizing CDR-H3 of 500 antibodies from the SAbDab dataset.
Method
Refine-GNN
MEAN
DilfAb
dyMEAN
Uni-Anti (ours)
AAG )
-2.16
-2.73
-2.84
-3.05
-3.87
14


Reviews:
[{"review_id": "Lir7CdAK11", "reviewer": "Reviewer_6abJ", "summary": "In this study, a transformer-based model utilizing protein structural information is proposed to predict the change in binding free energy of protein complexes (antibodies). The simpler architecture of their model as compared to the previous SOTA, allows for faster exploration of fitness landscape in the antibody design task. Their model trained with data augmentation, and teacher guidance by the previous SOTA (prompt-DDG) has been shown to have better performance in the prediction of change in binding free energy.\n\nAdditionally, a novel Mutation Explainer algorithm, utilizing the predictions of their transformer-based model, is proposed to score the marginal benefit of each mutation per site. In an application to SARS CoV-2, their algorithm is shown to be better than generative or energy-based models in ranking known effective mutants, and better than conditional generative models in optimizing antibody mutants against SARS CoV-2.\n\nTheir evaluations are accompanied by comprehensive ablation study on the choice of teacher model, inclusion of data augmentation, knowledge distillation and structural information in the training of predictive model.", "questions": "1. In the experiments of Table 6, the generated antibody mutants with different strategies are evaluated by the delta binding free energy predictor used to design the mutation explainer. This may not be a fair comparison, as it is kind of expected for the mutation explainer to perform well evaluated by the same predictor as the one that it is built upon. \nI understand that the ground truths are not available, however the performance evaluation should be independent of the model design.\n\n2. I am curious to know if you have examined the transfer from single to multiple site mutations in the prediction of delta binding free energy? if yes, how do the current models perform? This is a question of interest in protein property prediction tasks using mutagenesis datasets.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Generated an augmented SKEMPI-v2 dataset using previous SOTA model predictions on random mutations performed on SKEMPI-v2 annotated dataset. The augmented dataset was used for pretraining of their model.\n\n2. Demonstrating the utility of an effective predictor of delta binding free energy in antibody optimization and screening, i.e., mutant selection, without the need for generative modeling.\n\n3. Extensive benchmark on delta binding free energy prediction and ablation study on various design choices including type of teacher model, data augmentation, knowledge distillation and protein structural information.", "weaknesses": "1. Limited performance evaluation on anti-body design/optimization tasks.\n\n2. A discussion on how their prior based design can be combine with other techniques, such as generative modeling to improve antibody design\n\n3. Brief explanation as to why the bar is low (based on the range of correlation values in the tables) in the prediction of delta binding free energy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wCJyITLB7W", "reviewer": "Reviewer_Ceec", "summary": "This paper introduces Light-DDG, a lightweight ΔΔG predictor for protein-protein interactions, with a focus on antibody optimization. The authors address the challenge of efficient mutation screening by developing a simpler architecture that achieves faster inference while maintaining predictive performance through two key techniques: (1) knowledge distillation from more complex models like Prompt-DDG, and (2) supervised pre-training on an augmented dataset (SKEMPI-Aug) they create and release.\nThe main contributions include:\n\n- A lightweight Transformer-based ΔΔG predictor that achieves 89.7× faster inference compared to state-of-the-art models\n- A large-scale augmented mutation dataset (SKEMPI-Aug) containing ~640k annotated samples for supervised pre-training\n- An approach for explaining mutation preferences using iterative Shapley value estimation\n- *in silico* Experimental validation showing competitive or improved performance across multiple metrics, including per-structure Pearson correlation and RMSE\n- A case study demonstrating the model's application to SARS-CoV-2 antibody optimization\n\nThe authors demonstrate their method's effectiveness through *in silico* experiments, including ablation studies examining the impact of knowledge distillation and data augmentation, as well as analyses of structural noise robustness and architectural applicability.", "questions": "1. Can you provide evidence that all baseline models were trained on equivalent datasets to ensure fair comparison?\n2. How specifically did you prevent data leakage given Prompt-DDG's training history on SKEMPIv2.0?\n3. Can you provide comparisons against established optimization methods like CMA-ES and gradient-guided sampling?\n4. What justifies presenting this as a \"unified framework\" rather than an efficient implementation of a ΔΔG predictor?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Demonstrates effective application of augmentation and knowledge distillation to achieve significant inference speedup (89.7x) while maintaining or improving accuracy\n2. The simplified architecture enables interpretable explainability results (Figure 6)\n3. Shows promising results for combining architectural simplification with performance improvements through principled use of ML techniques", "weaknesses": "1. **Dataset and Baseline Comparison Issues**\n   The paper's central claims about performance improvements are undermined by inconsistent dataset usage across baselines. The ablation studies clearly demonstrate the outsized impact of the dataset choice, yet there is no clear indication that all baseline models were trained on equivalent data. This makes the comparative results in Table 2 difficult to interpret meaningfully.\n\n2. **Data Leakage Concerns**\n   There are serious methodological concerns about potential data leakage in the experimental setup. The authors use Prompt-DDG both as a teacher model and for dataset augmentation, but Prompt-DDG was itself trained on SKEMPIv2.0. This creates a circular dependency where test set information may be leaking into the training process through the augmented data, regardless of splitting schemes. The authors claim to avoid this via the \"cross-augmentation\" scheme [a technique I'm not aware of, nor was able to find reference for), depicted in Figure 2. However, I believe the authors need to explicitly address how they prevented this contamination from occurring, particularly given the ablation results that show the outsized impact of the augmentation on performance. \n\n3. **Inadequate Baseline Comparisons for Mutation Search**\n   The authors' approach of screening 10,000 random candidates significantly understates the complexity of the protein design problem and ignores established optimization algorithms. Given their efficient ΔΔG predictor as a fitness function, they should have compared against approaches like:\n   • CMA-ES (Covariance Matrix Adaptation Evolution Strategy)\n   • Gradient-guided Gibbs sampling \n   • Basic evolutionary algorithms with standard mutation operators\n\nThe strong performance of their random baseline in Table 6 suggests these evolutionary approaches could be quite effective. \n\n4. **Overstatement of Framework**\n   The authors' claim of developing a \"unified framework\" for antibody optimization overstates their contribution. What they demonstrate is primarily a successful application of knowledge distillation and data augmentation to improve model efficiency and generalization. While valuable, this falls short of the comprehensive framework they suggest.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LgEYlxpyBC", "reviewer": "Reviewer_tfwL", "summary": "This paper introduces a transformer architecture to predict $\\Delta\\Delta G$ (the differences in free energy binding due to mutation).  They use knowledge distillation to rather surprisingly obtain a much small, yet more accurate model than the large teach model it is trained on.  They demonstrate that having a light-weight $\\Delta\\Delta G$ predictor allows them to solve some important downstream tasks involving protein mutation prediction, protein evolution, etc.  There results beat the current state-of-the-art by a significant margin.", "questions": "Why does Light-DDG do so much better than Prompt-DGG given that it is trained using Prompt-DGG?  I know that there are claims that pupils in knowledge-distillation can be better than the teacher, but my experience is that this is rare and for there to be such a large improvement is unusual.  I am assuming that Light-DDG include components not in Prompt-DGG, but I did not pick up what makes the big difference.\n\nI'm also a bit lost why predicting $\\Delta \\Delta G$ should have such an impact.  Is the binding free energy the overwhelming factor in determining the effectiveness of an antibody?\n\nMy intuition would be that predicting $\\Delta \\Delta G$ would be very difficult as any mutation could cause a significant shape change which is hard to predict.  Thus, I would have thought your predictor would lead to only a moderate improvement.  Clearly my intuition or logic is wrong.  Where am I wrong (or why does your model work so well)?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "This is a strong paper with impressive results.  There should be of interest to people working in learning representation as it shows the effectiveness of using a transformer model to extract knowledge from a much larger model.  The experiments conducted are comprehensive and convincing.  There is a nice ablation study and various real world case studies showing the utility of the predictor.", "weaknesses": "I found the title difficult to comprehend because of the use of $\\Delta \\Delta G$, which I had no idea about.  Maybe this is common place and just reflects my ignorance, but it slowed down my ability to understand the paper.  This is a very minor comment which I'm happy for the authors to ignore.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sIPrLHQkAg", "reviewer": "Reviewer_Qvcz", "summary": "This paper proposes a simple and effective Light-DDG model to predict the binding free energy change of protein complexes. The method utilizes an augmented large-scale dataset as supervised pretraining and achieves SoTA performance through a simple knowledge distillation strategy. Additionally, a novel mutation explainer is proposed to design Uni-Anti, an antibody optimization framework that enables fast screening with Light-DDG, and the effectiveness of the framework is demonstrated through a case study of SARS-CoV-2.", "questions": "1.\tCould you clarify how the mutated structures (Mutant Str.) of Light-DDG in Table 4 are provided?\n2.\tWhile Prompt-DDG is a SoTA model, there may be potential for inaccuracies in pseudo-labeling. If so, would any methods be considered to address these?\n3.\tAdditionally, since augmentation could produce samples highly similar to SKEMPI v2.0, is there a filtering step in place to prevent overlaps? If not, would this be a consideration?\n4.\tThe dWJS [1] might apply to the antibody optimization task. Would a comparative analysis be possible?\n5.\tThe reference to the SAbDab dataset on p9 may be incorrect. Could you please verify?\n\n[1] Frey et al. (2024) \"Protein Discovery with Discrete Walk-Jump Sampling\", ICLR", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. This paper demonstrates the high predictive performance of binding free energy changes using a lightweight transformer-based predictor, enabled by supervised pretraining on a large-scale mutation dataset and knowledge distillation.\n2. The authors introduced an iterative Shapley value estimation algorithm, a coarse-to-fine iterative approach that effectively identifies key mutation sites, replacing the traditional Shapley value algorithm.\n3. The Uni-Anti framework enables rapid antibody optimization through fast screening without requiring a generative model, offering a practical solution for antibody design.", "weaknesses": "1. The backbone lightweight Transformer processes E(3)-invariant features, but the paper does not discuss whether the model guarantees E(3)-invariant outputs.\n2. Although the iterative Shapley value estimation algorithm is proposed to efficiently explore a large mutation space, it may lack a mathematical guarantee of accurately approximating Shapley values.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)