Proteins play crucial roles in every cellular process by interacting with each other, with nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that 3D models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting 3D structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.
Cryo-Electron Microscopy (cryo-EM) has become an extremely powerful method for resolving structural details of large biomolecular complexes. However, challenging problems in single-particle methods remain open because of (1) the low signal-to-noise ratio in EM; and (2) the potential anisotropy and lack of coverage of projection directions relative to the body-fixed coordinate system for some complexes. Whereas (1) is usually addressed by class averaging (and increasingly due to rapid advances in microscope and sensor technology), (2) is an artifact of the mechanics of interaction of biomolecul…
Geometric, topological and graph theory modeling and analysis of biomolecules are of essential importance in the conceptualization of molecular structure, function, dynamics, and transport. On the one hand, geometric modeling provides molecular surface and structural representation, and offers the basis for molecular visualization, which is crucial for the understanding of molecular structure and interactions. On the other hand, it bridges the gap between molecular structural data and theoretical/mathematical models. Topological analysis and modeling give rise to atomic critical points and con…
Modeling of biomolecular systems plays an essential role in understanding biological processes, such as ionic flow across channels, protein modification or interaction, and cell signaling. The continuum model described by the Poisson-Boltzmann (PB)/Poisson-Nernst-Planck (PNP) equations has made great contributions towards simulation of these processes. However, the model has shortcomings in its commonly used form and cannot capture (or cannot accurately capture) some important physical properties of biological systems. Considerable efforts have been made to improve the continuum model to accou…
Although algebraic graph theory based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties when compared with other quantitative approaches. There is a need to explore the capability and limitation of algebraic graph theory for molecular and biomolecular modeling, analysis, and prediction. In this work, we propose novel algebraic graph learning (AGL) models that encode high-dimensional physical and biological information into intrinsically low-dimensional representations. The propose…
Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery. The best-performing VS models depend vitally on three-dimensional (3D) structures, which are not available in general but can be obtained from molecular docking. However, current docking accuracy is relatively low, rendering unreliable VS models. We introduce sequence-based virtual screening (SVS) as a new generation of VS models for modeling biomolecular interactions. The SVS model utilizes advanced natural language processing (NLP) algorithms and optimizes de…
Motivation: Due to the central role of protein structure in molecular recognition, great computational efforts are devoted to modeling protein structures and motions that mediate structural rearrangements. The size, dimensionality, and non-linearity of the protein structure space present outstanding challenges. Such challenges also arise in robot motion planning, and robotics-inspired treatments of protein structure and motion are increasingly showing high exploration capability. Encouraged by such findings, we debut here ROMEO, which stands for Robotics prOtein Motion ExplOration framework. R…
Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection
University of British Columbia（不列颠哥伦比亚大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；NVIDIA（英伟达）；Peking University（北京大学）；Foundry Biosciences（Foundry 生物科技）；University of Utah（犹他大学）
Protein-nucleic acid complexes are important for many cellular processes including the most essential function such as transcription and translation. For many protein-nucleic acid complexes, flexibility of both macromolecules has been shown to be critical for specificity and/or function. Flexibility-rigidity index (FRI) has been proposed as an accurate and efficient approach for protein flexibility analysis. In this work, we introduce FRI for the flexibility analysis of protein-nucleic acid complexes. We demonstrate that a multiscale strategy, which incorporates multiple kernels to capture var…
SARS-CoV-2, the causative agent of the disease known as Covid-19, has so far reported around 3,435,000 cases of human infections, including more than 239,000 deaths in 187 countries, with no effective treatment currently available. For this reason, it is necessary to explore new approaches for the development of a drug capable of inhibiting the entry of the virus into the host cell. Therefore, this work includes the exploration of potential inhibitory compounds for the Spike protein of SARS-CoV-2 (PDB ID: 6VSB), which were obtained from The Patogen Box. Later, they were filtered through virtua…
This chapter reviews the differential geometry-based solvation and electrolyte transport for biomolecular solvation that have been developed over the past decade. A key component of these methods is the differential geometry of surfaces theory, as applied to the solvent-solute boundary. In these approaches, the solvent-solute boundary is determined by a variational principle that determines the major physical observables of interest, for example, biomolecular surface area, enclosed volume, electrostatic potential, ion density, electron density, etc. Recently, differential geometry theory has b…
Curvatures in the most probable rupture force ($f^*$) versus log-loading rate ($\log{r_f}$) observed in dynamic force spectroscopy (DFS) on biomolecular complexes are interpreted using a one-dimensional free energy profile with multiple barriers or a single barrier with force-dependent transition state. Here, we provide a criterion to select one scenario over another. If the rupture dynamics occurs by crossing a single barrier in a physical free energy profile describing unbinding, the exponent $ν$, from $(1- f^*/f_c)^{1/ν}\sim(\log r_f)$ with $f_c$ being a critical force in the absence of for…
A scheme for detection of abnormality in molecular nano-networks is proposed. This is motivated by the fact that early diagnosis, classification and detection of diseases such as cancer play a crucial role in their successful treatment. The proposed nano-abnormality detection scheme (NADS) comprises of a two-tier network of sensor nano-machines (SNMs) in the first tier and a data gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells as abnormality that is captured by variations in parameters of a nano-communications channel. In the second step, the SNMs transmit mi…
Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design
University of Florida（佛罗里达大学）；University of Chicago（芝加哥大学）；Data Science Institute, University of Chicago（芝加哥大学数据科学研究所）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；Mayo Clinic（梅奥诊所）；Argonne National Laboratory（阿贡国家实验室）
The activation of matrix metalloproteinase 2 (MMP2) is a crucial event during tumor metastasis and invasion, and this pathway network consists of 3 monomers. The pathway network of the activation obeys to a set of specified reaction rules. According to the rules, the individual molecules localize in a particular order and symmetrically around a homodimer following the formation of that dimer. We generalized the homodimer pathway network obeying to similar reaction rules, by changing the number of monomers involved in this pathway from 3 to N. At the previous work, we found the molecules in the…
FRET experiments can yield state-specific structural information on complex dynamic biomolecular assemblies. However, FRET experiments need to be combined with computer simulations to overcome their sparsity. We introduce (i) an automated FRET experiment design tool determining optimal FRET pairs for structural modeling, (ii) a protocol for efficient FRET-assisted computational structural modeling at multiple scales, and (iii) a quantitative quality estimate for judging the accuracy of determined structures. We tested against simulated and experimental data.
Representing local protein environments with machine learning force fields
IST Austria（IST奥地利研究院）；Technion, Israel（技术学院，以色列）；University of Oxford, UK（牛津大学，英国）；Princeton University（普林斯顿大学）；Broad Institute of MIT and Harvard（哈佛大学与麻省理工学院Broad研究所）
Computational methods are needed to differentiate the small fraction of missense mutations that contribute to disease by disrupting protein function from neutral variants. We describe several complementary methods using large-scale homology modeling of human protein complexes to detect non-neutral mutations. Importantly, unlike sequence conservation-based methods, this structure-based approach provides experimentally testable biochemical mechanisms for mutations in disease. Specifically, we infer metal ion, small molecule, protein-protein, and nucleic acid binding sites by homology and find th…
Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning
Nostrum Biodiscovery；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Faculty of Pharmacy and Food Sciences, University of Barcelona（巴塞罗那大学药学与食品科学系）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚研究与高级研究机构）；Data Science Dpt., Almirall S.A.（Almirall S.A.数据科学部门）
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）
We devise an approach for targeted molecular design, a problem of interest in computational drug discovery: given a target protein site, we wish to generate a chemical with both high binding affinity to the target and satisfactory pharmacological properties. This problem is made difficult by the enormity and discreteness of the space of potential therapeutics, as well as the graph-structured nature of biomolecular surface sites. Using a dataset of protein-ligand complexes, we surmount these issues by extracting a signature of the target site with a graph convolutional network and by encoding t…
Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Yet, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-graine…
Modeling of DNA-protein interactions is a complex process involving many important time and length scales. This can be facilitated through the use of coarse-grained models which reduce the number of degrees of freedom and allow efficient exploration of binding configurations. It is known that the local structure of DNA can significantly affect its protein-binding properties (i.e. intrinsic curvature in DNA-histone complexes). In a step towards comprehensive DNA-protein modeling, we expand the 3SPN.2 coarse-grained model to include intrinsic shape, and validate the refined model against experim…
Disordered proteins and nucleic acids play key roles in cellular function and disease. Here we review recent advances in the computational exploration of the conformational dynamics of flexible biomolecules. We focus on hierarchical chain growth (HCG) from fragment libraries built with atomistic molecular dynamics simulations. HCG combines chain fragments in a statistically reproducible manner into ensembles of full-length atomically detailed biomolecular structures. The input fragment structures are typically collected from molecular dynamics simulations, but could also come from structural d…
