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High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noise and high similarity in cell images impede the accuracy of deep learning-based image analysis. To address these issues, we introduce More Is Different (MID), a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. MID effectively combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and characterizing cell phenotypes with high precision. In comparative tests on drug-induced cardiotoxicity and mitochondrial toxicity classification, as well as compound classification, MID outperformed both DeepProfiler and CellProfiler, which are two highly recognized methods in HCA. We believe that our results demonstrate the utility and versatility of MID and anticipate its widespread adoption in HCA for advancing drug development and disease research.

Antibody represents a specific class of proteins produced by the adaptive immunity as a response to invading pathogens, and mining the information implied in antibody amino acid sequences can benefit both antibody property prediction and novel therapeutic development. Protein-specific pre-training models have been used to extract latent representations from protein sequences containing structural, functional, and homologous information. However, there is still room for improvement in pre-training models on antibody sequence. On the one hand, existing protein pre-training models mainly utilize pre-training language models without fully considering the differences between protein sequences and language sequences; on the other hand, in comparison with other proteins, antibodies possess their uniqueness, which should be incorporated using specifically designed training methods. Here, we present a pre-trained model of antibody sequences, Pre-training with A Rational Approach for antibodies (PARA), that employs a training strategy conforming to antibody sequences patterns and an advanced NLP self-encoding model structure. We show PARA’s performance on several tasks by comparing it to several published pre-trained models of antibodies. The results show that PARA significantly outperforms selected antibody pre-training models on these tasks, suggesting that PARA has an advantage in capturing antibody sequence information. To the best of our knowledge, PARA is the first antibody language model that takes into account the features of antibody sequences. We believe that the antibody latent representation provided by PARA can substantially facilitate the studies in relevant areas, such as antibody structure prediction, affinity prediction, and antibody de novo design.

Proteins are one of the most important components in life, and the research on protein complex and the development of protein or antibody drugs relies on effective representation of proteins. Both experimental methods like cryo-electron microscopy and computational methods like molecular dynamic simulation suffer from high cost, long time investment and low throughput, and cannot be used in large-scale studies. Some examples of artificial intelligence for protein complex prediction tasks show that different representations of proteins have their own limitations. This paper constructs a multimodal model named WUREN (Whole-modal fUsion Representation for protein interaction interfacE predictioN), which effectively fuses sequence, graph, and structural features. WUREN has achieved state-of-the-art performance on both the antigen epitope prediction task and the protein-protein interaction interface prediction task, with AUC-PR reaching 0.462 and 0.516, respectively. Our results show that WUREN is a general and effective feature extraction model for protein complex, which can be used in the development of protein-based drugs. Furthermore, the general framework in WUREN can be potentially applied to model similar biologics to proteins, such as DNA and RNA.

The RBD (receptor binding domain) of the SARS-CoV-2 virus S (spike) protein mediates viral cell attachment and serves as a promising target for therapeutics development. Mutations on the S-RBD may alter its affinity to the cell receptor and affect the potency of vaccines and antibodies. Here we used an in silico approach to predict how mutations on RBD affect its binding affinity to hACE2 (human angiotensin-converting enzyme2). The effect of all single point mutations on the interface was predicted. SPR assay results show that 6 out of 9 selected mutations can strengthen binding affinity. Our prediction has reasonable agreement with the previous deep mutational scan results and recently reported mutants. Our work demonstrated the in silico method as a powerful tool to forecast more powerful virus mutants, which will significantly benefit the development of broadly neutralizing vaccine and antibody.

BACKGROUND: HER2 transmembrane domain (TMD) mutation has been reported as a rare driver mutation associated with advanced stage disease and a poor prognosis in patients with lung adenocarcinoma (LUAD). We aimed to comprehensively profile the genetic landscape and treatment response information of HER2 TMD-mutant LUAD. METHODS: An in-house database of 7,812 LUAD patients was screened for mutation prevalence. A multi-center cohort of 16 HER2 V659E-mutant patients and an external cohort of 38 HER2-mutant patients from cBioPortal with overall survival (OS) data were analyzed. Eight patients from the in-house cohort were included in the real-world study of treatment response. Molecular docking simulation and binding affinity prediction were performed. RESULTS: In Chinese LUAD, the prevalence of HER2 TMD mutation was 0.18% (14/7,812), and 0.14% (11/7,812) for the HER2 V659E mutation. The most recurrent co-alteration was TP53 mutation (n=4, 25%) and HER2 amplification (n=2, 12.5%). TMD-mutant patients were diagnosed at more advance stages (P

The RBD (receptor binding domain) of the SARS-CoV-2 virus S (spike) protein mediates viral cell attachment and serves as a promising target for therapeutics development. Mutations on the S-RBD may alter its affinity to the cell receptor and affect the potency of vaccines and antibodies. Here we used an

The RBD (receptor binding domain) of the SARS-CoV-2 virus S (spike) protein mediates the viral cell attachment and serves as a promising target for therapeutics development. Mutations on the S-RBD may alter its affinity to cell receptor and affect the potency of vaccines and antibodies. Here we used an in-silico approach to predict how mutations on RBD affect its binding affinity to hACE2 (human angiotensin-converting enzyme2). The effect of all single point mutations on the interface was predicted. SPR assay result shows that 6 out of 9 selected mutations can strengthen binding affinity. Our prediction has reasonable agreement with the previous deep mutational scan results and recently reported mutants. Our work demonstrated in silico method as a powerful tool to forecast more powerful virus mutants, which will significantly benefit for the development of broadly neutralizing vaccine and antibody.

The functions and elasticities of the cell are largely related to the structures of the cytoskeletons underlying the lipid bilayer. Among various cell types, the red blood cell (RBC) possesses a relatively simple cytoskeletal structure. Underneath the membrane, the RBC cytoskeleton takes the form of a two-dimensional triangular network, consisting of nodes of actins (and other proteins) and edges of spectrins. Recent experiments focusing on the malaria-infected RBCs (iRBCs) show that there is a correlation between the elongation of spectrins in the cytoskeletal network and the stiffening of the iRBCs. Here we rationalize the correlation between these two observations by combining the wormlike chain model for single spectrins and the effective medium theory for the network elasticity. We specifically focus on how the disorders in the cytoskeletal network affect its macroscopic elasticity. Analytical and numerical solutions from our model reveal that the stiffness of the membrane increases with increasing end-to-end distances of spectrins, but has a nonmonotonic dependence on the variance of the end-to-end distance distributions. These predictions are verified quantitatively by our atomic force microscopy and micropipette aspiration measurements of iRBCs. The model may, from a molecular level, provide guidelines for future identification of new treatment methods for RBC-related diseases, such as malaria infection.

e21521 Background: ERBB2 mutations, mostly affecting the kinase domain (KD), were later found in the transmembrane domain (TMD), most frequently seen in residues V659, G660, but with a much lower prevalence. Comparing to other solid tumors, ERBB2-mutant NSCLC shows a lower response rate to HER2 inhibitors (HER2i). ERBB2 V659E has been reported to respond to lapatinib in combination with carpecitabine, afatinib, or ado-trastuzumab emtansine (TDM-1). However, since the prevalence of ERBB2 V659E is low, no study has reported the subsequent clinical management after developing resistance to HER2i. Methods: We retrospectively screened genomic profiles of 7,812 lung adenocarcinoma (LADC) patients. Molecular docking of lapatinib, afatinib and pyrotinib with the HER2 KD was simulated with AutoDock 4.2 and the binding affinity was predicted by MMPBSA.py program in AmberTools18. Results: We analyzed NGS data of 7812 LADC patients and identified 14 patients (0.18%) with HER2 TMD mutation, mostly V659E mutation (11/7812, 0.14%); other mutations included V664F, T652M, V664F; each occurred in 1 patient. In this cohort, all adenocarcinomas harbor a dinucleotide missense mutation, including 10 TT→AA and 6 TT→AG (c.1976_1977). T P53 was the most frequently co-mutated gene (n = 4) followed by HER2 (n = 2), NSD1 (n = 2) and RARA (n = 2). Treatment history of 9 advanced LADC patients were collected and analyzed. 3 of them had chemotherapy as first-line therapy. 5 patients were treated with HERi as first-line therapy, achieving a median PFS of 192 days (ranged from 60-515days), comparing to patients treated with chemotherapy (71.7±53.3days). 1 patient who was treated with different HERi sequentially in the order of lapatinib, afatinib, TDM-1, achieving an overall PFS of 515 days. Subsequently, she was treated with pyrotinib and had a PFS of 92 days. Molecular docking simulation showed the covalent inhibitors Afatinib and Pyrotinib are more favorable for blocking HER2 KD than non-covalent inhibitor Lapatinib. Between the two covalent inhibitors, Pyrotinib (∆GPyrotinib = -65.92kcal/mol) shows stronger non-covalent binding ability than Afatinib (∆GAfatinib = -59.71 kcal/mol) (p < 0.001). Conclusions: To the best of our knowledge, this is the largest-reported HER2 V659E mutation cohort. We revealed the prevalence of ERBB2 V659E in Chinese LUAD patients and elucidated the genomic profile of ERBB2 V659E mutant. This study paves avenue for the utilization of the novel HER2 inhibitors pyrotinib in ERBB2 V659E mutant LUAD patients.

Active matter such as swarming bacteria and motile colloids exhibits exotic properties different from conventional equilibrium materials. Among these properties, the enhanced diffusion of tracer particles is generally deemed as a hallmark of active matter. Here, rather than spherical tracers, we investigate the diffusion of isolated ellipsoids in quasi-two-dimensional bacterial bath. Our study reveals a nonlinear enhancement of both translational and rotational diffusions. More importantly, we uncover an anomalous coupling between translation and rotation that is strictly prohibited in the classic Brownian diffusion. Combining experiments with theoretical modeling, we show that such an anomaly arises from generic stretching flows induced by swimming bacteria. Our work illustrates a universal organizing principle of active matter and sheds new light on fundamental transport processes in microbiological systems., Comment: 13 pages, 4 figures

The macroscopic properties, the properties of individual components and how those components interact with each other are three important aspects of a composited structure. An understanding of the interplay between them is essential in the study of complex systems. Using axonal cytoskeleton as an example system, here we perform a theoretical study of slender structures that can be coarse-grained as a simple smooth 3-dimensional curve. We first present a generic model for such systems based on the fundamental theorem of curves. We use this generic model to demonstrate the applicability of the well-known worm-like chain (WLC) model to the network level and investigate the situation when the system is stretched by strong forces (weakly bending limit). We specifically studied recent experimental observations that revealed the hitherto unknown periodic cytoskeleton structure of axons and measured the longitudinal fluctuations. Instead of focusing on single molecules, we apply analytical results from the WLC model to both single molecule and network levels and focus on the relations between extensions and fluctuations. We show how this approach introduces constraints to possible local dynamics of the spectrin tetramers in the axonal cytoskeleton and finally suggests simple but self-consistent dynamics of spectrins in which the spectrins in one spatial period of axons fluctuate in-sync., Comment: 18 pages, 4 figures

The presence of slight azimuthal asymmetry in the initial shape of an underwater bubble entirely alters the final breakup dynamics. Here I examine the influence of initial asymmetry on the final breakup by simulating the bubble surface evolution as a Hamiltonian evolution corresponding to an inviscid, two-dimensional, planar implosion. I find two types of breakups: a previously reported coalescence mode in which distant regions along the air-water surface curve inwards and eventually collide with finite speed, and a hitherto unknown cusp-like mode in which the surface develops sharp tips whose radii of curvature are much smaller than the average neck radius. I present three sets of results that characterize the nature of this cusp mode. First, I show that the cusp mode corresponds to a saddle-node. In other words, an evolution towards a cross-section shape with sharp tips invariably later evolves away from it. In phase space, this saddle-node separates coalescence modes whose coalescence planes lie along different spatial orientations. Second, I show that the formation of the sharp tips can be interpreted as a weakly first-order transition which becomes second-order, corresponding to the formation of a finite-time curvature singularity, in the limit that the initial perturbation amplitude approaches zero. Third I show that, as the curvature singularity is approached, the maximum surface curvature diverges approximately as $(t_c - t)^{-0.8}$, where $t_c$ is the onset time of the singularity and the maximum velocity diverges approximately as $(t_c-t)^{-0.4}$. In practice, these divergences imply that viscous drag and compressibility of the gas flow, two effects not included in my analysis, become significant as the interface evolves towards the curvature singularity., 34 pages, 11 figures, submitted to Physics of Fluids

The disconnection of an underwater bubble illustrates how slight initial asymmetries can prevent the formation of a finite-time singularity. Creating a singularity by focusing a finite amount of energy dynamically into a vanishingly small amount of material requires that the initial condition be perfectly symmetric. In reality, imperfections are always present. We show a slight azimuthal asymmetry in the initial shape of the bubble neck excites vibrations that persist over time. As a result, the focusing singularity is generically preempted by a smooth contact.

Malaria is one of the most widespread and deadly human parasitic diseases caused by the Plasmodium (P.) species with the P.falciparum being the most deadly. The parasites are capable of invading red blood cells (RBCs) during infection. At the late stage of parasites’ development, the parasites export proteins to the infected RBCs (iRBC) membrane and bind to receptors of surface proteins on the endothelial cells that line microvasculature walls. Resulting adhesion of iRBCs to microvasculature is one of the main sources of most complications during malaria infection. Therefore, it is important to develop a versatile and simple experimental method to quantitatively investigate iRBCs cytoadhesion and binding kinetics. Here, we developed an advanced flow based adhesion assay to demonstrate that iRBC’s adhesion to endothelial CD36 receptor protein coated channels is a bistable process possessing a hysteresis loop. This finding confirms a recently developed model of cell adhesion which we used to fit our experimental data. We measured the contact area of iRBC under shear flow at different stages of infection using Total Internal Reflection Fluorescence (TIRF), and also adhesion receptor and ligand binding kinetics using Atomic Force Microscopy (AFM). With these parameters, we reproduced in our model the experimentally observed changes in adhesion properties of iRBCs accompanying parasite maturation and investigated the main mechanisms responsible for these changes, which are the contact area during the shear flow as well as the rupture area size., Global Enterprise for Micro-Mechanics and Molecular Medicine, United States. Dept. of Defense (DOD-ARO (W 911 NF-09-0480)), Singapore–MIT Alliance for Research and Technology ((SMART) Fellowship), National Science Foundation (U.S.) (NSF Grant No.1112825)