Machine learning-guided directed evolution for protein engineering

Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.Comment: Made significant revisions to focus on aspects most relevant to applying machine learning to speed up directed evolutio

Analysis of Three-Dimensional Protein Images

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.Comment: See http://www.jair.org/ for any accompanying file

생물학적 서열 데이터에 대한 표현 학습

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 윤성로.As we are living in the era of big data, the biomedical domain is not an exception. With the advent of technologies such as next-generation sequencing, developing methods to capitalize on the explosion of biomedical data is one of the most major challenges in bioinformatics. Representation learning, in particular deep learning, has made significant advancements in diverse fields where the artificial intelligence community has struggled for many years. However, although representation learning has also shown great promises in bioinformatics, it is not a silver bullet. Off-the-shelf applications of representation learning cannot always provide successful results for biological sequence data. There remain full of challenges and opportunities to be explored. This dissertation presents a set of representation learning methods to address three issues in biological sequence data analysis. First, we propose a two-stage training strategy to address throughput and information trade-offs within wet-lab CRISPR-Cpf1 activity experiments. Second, we propose an encoding scheme to model interaction between two sequences for functional microRNA target prediction. Third, we propose a self-supervised pre-training method to bridge the exponentially growing gap between the numbers of unlabeled and labeled protein sequences. In summary, this dissertation proposes a set of representation learning methods that can derive invaluable information from the biological sequence data.우리는 빅데이터의 시대를 맞이하고 있으며, 의생명 분야 또한 예외가 아니다. 차세대 염기서열 분석과 같은 기술들이 도래함에 따라, 폭발적인 의생명 데이터의 증가를 활용하기 위한 방법론의 개발은 생물정보학 분야의 주요 과제 중의 하나이다. 심층 학습을 포함한 표현 학습 기법들은 인공지능 학계가 오랫동안 어려움을 겪어온 다양한 분야에서 상당한 발전을 이루었다. 표현 학습은 생물정보학 분야에서도 많은 가능성을 보여주었다. 하지만 단순한 적용으로는 생물학적 서열 데이터 분석의 성공적인 결과를 항상 얻을 수는 않으며, 여전히 연구가 필요한 많은 문제들이 남아있다. 본 학위논문은 생물학적 서열 데이터 분석과 관련된 세 가지 사안을 해결하기 위해, 표현 학습에 기반한 일련의 방법론들을 제안한다. 첫 번째로, 유전자가위 실험 데이터에 내재된 정보와 수율의 균형에 대처할 수 있는 2단계 학습 기법을 제안한다. 두 번째로, 두 염기 서열 간의 상호 작용을 학습하기 위한 부호화 방식을 제안한다. 세 번째로, 기하급수적으로 증가하는 특징되지 않은 단백질 서열을 활용하기 위한 자기 지도 사전 학습 기법을 제안한다. 요약하자면, 본 학위논문은 생물학적 서열 데이터를 분석하여 중요한 정보를 도출할 수 있는 표현 학습에 기반한 일련의 방법론들을 제안한다.1 Introduction 1 1.1 Motivation 1 1.2 Contents of Dissertation 4 2 Background 8 2.1 Representation Learning 8 2.2 Deep Neural Networks 12 2.2.1 Multi-layer Perceptrons 12 2.2.2 Convolutional Neural Networks 14 2.2.3 Recurrent Neural Networks 16 2.2.4 Transformers 19 2.3 Training of Deep Neural Networks 23 2.4 Representation Learning in Bioinformatics 26 2.5 Biological Sequence Data Analyses 29 2.6 Evaluation Metrics 32 3 CRISPR-Cpf1 Activity Prediction 36 3.1 Methods 39 3.1.1 Model Architecture 39 3.1.2 Training of Seq-deepCpf1 and DeepCpf1 41 3.2 Experiment Results 44 3.2.1 Datasets 44 3.2.2 Baselines 47 3.2.3 Evaluation of Seq-deepCpf1 49 3.2.4 Evaluation of DeepCpf1 51 3.3 Summary 55 4 Functional microRNA Target Prediction 56 4.1 Methods 62 4.1.1 Candidate Target Site Selection 63 4.1.2 Input Encoding 64 4.1.3 Residual Network 67 4.1.4 Post-processing 68 4.2 Experiment Results 70 4.2.1 Datasets 70 4.2.2 Classification of Functional and Non-functional Targets 71 4.2.3 Distinguishing High-functional Targets 73 4.2.4 Ablation Studies 76 4.3 Summary 77 5 Self-supervised Learning of Protein Representations 78 5.1 Methods 83 5.1.1 Pre-training Procedure 83 5.1.2 Fine-tuning Procedure 86 5.1.3 Model Architecturen 87 5.2 Experiment Results 90 5.2.1 Experiment Setup 90 5.2.2 Pre-training Results 92 5.2.3 Fine-tuning Results 93 5.2.4 Comparison with Larger Protein Language Models 97 5.2.5 Ablation Studies 100 5.2.6 Qualitative Interpreatation Analyses 103 5.3 Summary 106 6 Discussion 107 6.1 Challenges and Opportunities 107 7 Conclusion 111 Bibliography 113 Abstract in Korean 130박

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft