Less than 1% of protein sequences are structurally and functionally annotated. Natural Language Processing (NLP) community has recently embraced self-supervised learning as a powerful approach to learn representations from unlabeled text, in large part due to the attention-based context-aware Transformer models. In this work we present a modification to the RoBERTa model by inputting during pre-training a mixture of binding and non-binding protein sequences (from STRING database). However, the sequence pairs have no label to indicate their binding status, as the model relies solely on Masked Language Modeling (MLM) objective during pre-training. After fine-tuning, such approach surpasses models trained on single protein sequences for protein-protein binding prediction, TCR-epitope binding prediction, cellular-localization and remote homology classification tasks. We suggest that the Transformer's attention mechanism contributes to protein binding site discovery. Furthermore, we compress protein sequences by 64% with the Byte Pair Encoding (BPE) vocabulary consisting of 10K subwords, each around 3-4 amino acids long. Finally, to expand the model input space to even larger proteins and multi-protein assemblies, we pre-train Longformer models that support 2,048 tokens. Further work in token-level classification for secondary structure prediction is needed. Code available at: https://github.com/PaccMann/paccmann_proteomics
Protein-protein binding enables orderly and lawful biological self-organization, and is therefore considered a miracle of nature. Protein-protein binding is steered by electrostatic forces, hydrogen bonding, van der Waals force, and hydrophobic interactions. Among these physical forces, only the hydrophobic interactions can be considered as long-range intermolecular attractions between proteins in intracellular and extracellular fluid. Low-entropy regions of hydration shells around proteins drive hydrophobic attraction among them that essentially coordinate protein-protein docking in rotationa…
Structure-Based Function Prediction of Functionally Unannotated Structures in the PDB: Prediction of ATP, GTP, Sialic Acid, Retinoic Acid and Heme-bound and -Unbound (Free) Nitric Oxide Protein Binding Sites
An Automatable Analytical Algorithm for Structure-Based Protein Functional Annotation via Detection of Specific Ligand 3D Binding Sites: Application to ATP (ser/thr Protein Kinases) and GTP (Small Ras-type G-Proteins) Binding Sites
Prediction of Flavin Mononucleotide (FMN) Binding Sites in Proteins Using the 3D Search Motif Method and Double-Centroid Reduced Representation of Protein 3D Structures
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by the binding ligand. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a novel method that represents protein behavioral change upon ligand binding with a simple feature that can be used to predict protein-ligand affinity. From unbiased molecular simulation data, an unsupervis…
The energy landscape of DNA-binding proteins along the genome
Istituto Europeo di Oncologia（欧洲肿瘤研究所）；Heidelberg Institute for Theoretical Studies (HITS)（海德堡理论研究所）；Heidelberg University（海德堡大学）；Università degli Studi di Milano（米兰大学）；INFN（意大利国家核物理研究所）
Because human serum transferrin (hTF) exists freely in serum, it is a potential target for cancer treatment drugs and in curing iron-overloaded conditions in patients via long-term transfusion therapy. The understanding of the interactions between hTF and metal ions is very important for biological, pharmalogical, toxicological, and other protein engineering purposes. In this paper, a simple linear free energy correlation is proposed to predict the binding strength between hTF protein and metal cations. The stability constants for a family of metal-hTF complexes can be correlated to the non-so…
Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Central China Research Institute for Artificial Intelligence Technologies（中原人工智能产业技术研究院）
The major histocompatibility complex (MHC) molecules, which bind peptides for presentation on the cell surface, play an important role in cell-mediated immunity. In light of developing databases and technologies over the years, significant progress has been made in research on peptide binding affinity calculation. Several in techniques have been developed to predict peptide binding to MHC class I. Most of the research on MHC Class I due to its nature brings better performance and more. Considering the use of different methods and different technologies, and the approach of similar methods on d…
Intrinsically disordered proteins participate in many biological processes by folding upon binding with other proteins. However, coupled folding and binding processes are not well understood from an atomistic point of view. One of the main questions is whether folding occurs prior to or after binding. Here we use a novel unbiased high-throughput adaptive sampling approach to reconstruct the binding and folding between the disordered transactivation domain of \mbox{c-Myb} and the KIX domain of the CREB-binding protein. The reconstructed long-term dynamical process highlights the binding of a sh…
University of Illinois at Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；Tsinghua University（清华大学）
Modeling the effects of mutations on the binding affinity plays a crucial role in protein engineering and drug design. In this study, we develop a novel deep learning based framework, named GraphPPI, to predict the binding affinity changes upon mutations based on the features provided by a graph neural network (GNN). In particular, GraphPPI first employs a well-designed pre-training scheme to enforce the GNN to capture the features that are predictive of the effects of mutations on binding affinity in an unsupervised manner and then integrates these graphical features with gradient-boosting tr…
Accurately determining a change in protein binding affinity upon mutations is important for the discovery and design of novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be aided with computational methods. Most of the computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore the sequence-based prediction of change in protein binding affinity upon…
Binding of ligands is often crucial for function yet the effects of ligand binding on the mechanical stability and energy landscape of proteins are incompletely understood. Here we use a combination of single-molecule optical tweezers and MD simulations to investigate the effect of ligand binding on the energy landscape of acyl-coenzyme A (CoA) binding protein (ACBP). ACBP is a topologically simple and highly conserved four-alpha-helix bundle protein that acts as an intracellular transporter and buffer for fatty-acyl CoA and is active in membrane assembly. We have previously described the beha…
Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. But despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics and genetics, and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination - by varying degrees of flexibility and shape/chemistry complementarity - but the key ingredient is…
