搜索结果：Doklady. Biochemistry and biophysics

British biophysics has a tradition of scientific invention and innovation, resulting in new technologies transforming biological insight, such as rapid DNA sequencing, super-resolution and label-free microscopy, high-throughput and single-molecule bio-sensing, and bio-inspired synthetic materials. Some advances were established through democratised platforms and many have biomedical success, a key example involving the SARS-CoV-2 spike protein during the COVID-19 pandemic. Here, three UK labs made crucial contributions revealing how the spike protein targets human cells, and how therapies of vaccines and neutralizing nanobodies work, enabled largely through biophysical innovations of cryo-electron microscopy. Here, we discuss leading-edge innovations which resulted from discovery-led British 'Physics of Life' research (capturing blends of physical-life sciences research in the UK including biophysics and biological physics) and have matured into wide-reaching sustainable commercial ventures enabling translational impact. We describe the biophysical science which led to these academic spinouts, presenting the scientific questions that were addressed through innovating new techniques

This study investigates the impact of educational comics as an active learning strategy in physics workshops for undergraduate students in Chemistry and Pharmacy and Biochemistry during the second semester of 2025. Conceptual understanding was assessed using the Force Concept Inventory (FCI), and student motivation and attitudes toward physics were evaluated through a Likert-type survey administered in pre- and post-test formats. The results show an average normalized gain of g = 0.21 on the FCI, corresponding to a low-to-medium range according to physics education research. A higher gain is observed in items directly related to the intervened content (g = 0.23) compared to non-intervened items (g = 0.19), suggesting that instructional design influences domain-specific conceptual development. At the motivational level, improvements are observed in student interest, self-efficacy, and perceived usefulness of physics, along with a reduction in negative emotional responses toward the subject. These findings indicate that educational comics can serve as an effective pedagogical scaffold, promoting positive learning dispositions and supporting targeted conceptual development in non-phys

This report is a serious call to scientists, innovators, investors, and policymakers to invest in the development of biophysics in Africa. The complex problems of our day demand multidisciplinary approaches, and biophysics offers training in much-needed multi- and cross-disciplinary thinking. Biophysics is a research field at the forefront of modern science because it provides a powerful scientific platform that addresses many of the critical challenges humanity faces today and in the future. It is a vital source of innovation for any country interested in developing a high-tech economy. However, there is woefully little biophysics educational and research activity in Africa, representing a critical gap that must be addressed with urgency. This report suggests key research areas that African biophysicists should focus on, identifies major challenges to growing biophysics in Africa, and underscores the high-priority needs that must be addressed.

Quantitative modeling has become an essential tool in modern biophysics, driven by advances in both experimental techniques and theoretical frameworks. Powerful high-resolution techniques now provide detailed datasets spanning molecular to tissue scales, allowing to visualize cellular structures with unprecedented detail. In parallel, developments in soft and active matter physics have established a robust theoretical basis for describing biological systems. In this context, two main modeling paradigms have emerged: particle-based models, which explicitly represent discrete components and their interactions, and continuum models, which describe systems through spatially varying fields. We compare these approaches across biological scales, highlighting their respective strengths, limitations, and domains of applicability. To keep our discussion biologically relevant, we focus on five systems of fundamental importance: the cytoskeleton, membranes, chromatin, biomolecular condensates and tissues. With this Review, we thus aim to provide a framework for both theorists and experimentalists to select appropriate modeling strategies, and highlight future directions in biophysical modeling

In computational biophysics, where molecular data is expanding rapidly and system complexity is increasing exponentially, large language models (LLMs) and agent-based systems are fundamentally reshaping the field. This perspective article examines the recent advances at the intersection of LLMs, intelligent agents, and scientific computation, with a focus on biophysical computation. Building on these advancements, we introduce ADAM (Agent for Digital Atoms and Molecules), an innovative multi-agent LLM-based framework. ADAM employs cutting-edge AI architectures to reshape scientific workflows through a modular design. It adopts a hybrid neural-symbolic architecture that combines LLM-driven semantic tools with deterministic symbolic computations. Moreover, its ADAM Tool Protocol (ATP) enables asynchronous, database-centric tool orchestration, fostering community-driven extensibility. Despite the significant progress made, ongoing challenges call for further efforts in establishing benchmarking standards, optimizing foundational models and agents, building an open collaborative ecosystem and developing personalized memory modules. ADAM is accessible at https://sidereus-ai.com.

This is a provisional status report of biophysics activities in Africa. We start by highlighting the importance of biophysics research and development for every country's economy in the 21st century. Yet, the amount of biophysics activity in African countries varies between woefully little to nothing at all. We present a scope of biophysics research on the continent based on a pilot scientometrics study. We discuss a number of existing multinational programmes and infrastructure initiatives and propose a Pan African Professional Society for Biophysics. We emphasize the need for education, infrastructure and career development, and conclude with a list of suggested recommendations for expedited development of biophysics research on the continent.

Antibody-based therapeutics-including antibody-drug conjugates (ADCs), bispecific antibodies, and novel formats-are reshaping oncology, yet key determinants of efficacy, safety, and manufacturability frequently emerge after conjugation and formulation. We argue that computational biophysics provides an underexploited framework to address this gap by connecting molecular interactions to biological outcomes. We highlight how molecular dynamics, coarse-grained simulations, and free energy calculations reveal how conjugation site, linker chemistry, and drug-antibody ratio reshape conformational landscapes. We emphasize structural coupling between antibody, linker, and payload, with implications for antigen binding, internalization, and developability. We propose that integrating physics-based modeling into development pipelines-alongside experimental validation-can reduce empirical iteration and de-risk translation. As force fields, and hybrid physics-machine-learning methods improve, this field is poised to become a central driver of next-generation ADC design.

With many advancements in in silico biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical applications. Achieving models that characterize the link of molecular interactions to the activity and structure of a whole organ are termed multiscale biophysics. Historically, the pharmaceutical industry has worked well with in silico models by leveraging their prediction capabilities for drug testing. However, the needed higher fidelity and higher resolution of models for efficient prediction of pharmacological phenomenon dictates that in silico approaches must account for the verifiable multiscale biophysical phenomena, as a spatial and temporal dimension variation for different processes and models. The collection of different multiscale models for different tissues and organs can compose digital twin solutions towards becoming a service for researchers, clinicians, and drug developers. Our paper has two main goals: 1) To clarify to what extent detailed single- and multiscale modeling has been accomplished thus far, we provide a review on this topic focusing on the biophysics of epithelial, cardiac, and

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these novel AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a novel computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers, and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to

Biological molecules, like all active matter, use free energy to generate force and motion which drive them out of thermal equilibrium, and undergo inherent dynamic interconversion between metastable free energy states separated by levels barely higher than stochastic thermal energy fluctuations. Here, we explore the founding and emerging approaches of the field of single-molecule biophysics which, unlike traditional ensemble average approaches, enable the detection and manipulation of individual molecules and facilitate exploration of biomolecular heterogeneity and its impact on transitional molecular kinetics and underpinning molecular interactions. We discuss the ground-breaking technological innovations which scratch far beyond the surface into open questions of real physiology, that correlate orthogonal data types and interplay empirical measurement with theoretical and computational insights, many of which are enabling artificial matter to be designed inspired by biological systems. And finally, we examine how these insights are helping to develop new physics framed around biology.

In this project, we present a deep neural network (DNN)-based biophysics model that uses multi-scale and uniform topological and electrostatic features to predict protein properties, such as Coulomb energies or solvation energies. The topological features are generated using element-specific persistent homology (ESPH) on a selection of heavy atoms or carbon atoms. The electrostatic features are generated using a novel Cartesian treecode, which adds underlying electrostatic interactions to further improve the model prediction. These features are uniform in number for proteins of varying sizes; therefore, the widely available protein structure databases can be used to train the network. These features are also multi-scale, allowing users to balance resolution and computational cost. The optimal model trained on more than 17,000 proteins for predicting Coulomb energy achieves MSE of approximately 0.024, MAPE of 0.073 and $R^2$ of 0.976. Meanwhile, the optimal model trained on more than 4,000 proteins for predicting solvation energy achieves MSE of approximately 0.064, MAPE of 0.081, and $R^2$ of 0.926, showing the efficiency and fidelity of these features in representing the protein s

Hackathons are intensive innovation-oriented events where participants work in teams to solve problems or create projects in as little as 24 or 48 hours. These events are common in startup culture, open source communities and mainstream industry. Here we examine how hackathons can be ported to academic teaching, specifically in computational biophysics. We propose hackathons as a teaching modality distinct from traditional courses and structured workshops. In particular, we suggest they can offer a low-stakes platform for students to overcome entry barriers to computational tools or to explore new topics, disciplines, and skills beyond their academic comfort zone. We tested this format in two computational biophysics hackathons on the Göttingen campus in 2023 and 2024, providing practical insights and a preliminary evaluation. To the best of our knowledge, the 2024 event is the first public hackathon dedicated to Biophysics. This paper explores the benefits of the hackathon format for teachers and researchers and provides guidelines for running a hackathon adapted to a teaching goal.

Various panel sessions were organized to highlight the activities of the African Strategy for Fundamental and Applied Physics (ASFAP) Working Groups during the second African Conference of Fundamental and Applied Physics (ACP2021) that was held in March 7-11, 2022. A joint session was devoted to highlight the activities assigned to the Light Sources, Accelerators, Biophysics, Earth Sciences, Atomic and Molecular Physics, and Condensed Matter and Materials Physics Working Groups. Major outcomes and recommendations are demonstrated and deliberated in this contribution.

Phase-separated liquid droplets organize molecules in cells, but the underlying physical principles differ from abiotic mixing and quantitative rules in living systems remain poorly understood. The pyrenoid -- a liquid-like organelle that enhances photosynthetic carbon fixation in algae and hornworts -- provides an unusually tractable model system. Here, we review recent advances in our understanding of pyrenoids from the perspective of biophysics. We highlight how reaction-diffusion models connect compartment architecture to catalytic performance, how soft matter theories link molecular interactions to condensate assembly, and how modern experimental methods enable these predictions to be tested quantitatively. Recent studies suggest that pyrenoid function may be described by a small number of effective transport and reaction processes, while condensate assembly can be understood through molecular design parameters and thermodynamic constraints. Together, these findings establish the pyrenoid as a powerful system for investigating catalytic compartmentalization, biomolecular self-organization and the emergence of effective physical descriptions in living systems.

Cartilage is a connective tissue that covers the surfaces of bones in joints and provides a smooth gliding surface for movement. It is characterized by specific biophysical properties that allow it to withstand compressive loads, distribute mechanical forces, and maintain tissue integrity. The bi-ophysical properties of cartilage are primarily determined by its extracellular matrix, which is composed of collagen fibers, proteoglycans, and water. The collagen fibers provide tensile strength, the proteoglycans provide compressive resistance, and the water content provides lubrication and shock absorption. The potential for greater knowledge of cartilage function through refinement and engineering-level understanding could inform the design of interventions for cartilage dysfunction and pathology. The aim is to assist to present basic principles of cartilage modeling and discussing the underlying physics and assumptions with relatively simple settings, and also it presents the derivation of multiphase cartilage models that are consistent with the discussions. Furthermore, modern developments align the structure captured in the models with observed complexities. The interactions betwee

The future development of an AI scientist, a tool that is capable of integrating a variety of experimental data and generating testable hypotheses, holds immense potential. So far, bespoke machine learning models have been created to specialize in singular scientific tasks, but otherwise lack the flexibility of a general purpose model. Here, we show that a general purpose large language model, chatGPT 3.5-turbo, can be fine-tuned to learn the structural biophysics of DNA. We find that both fine-tuning models to return chain-of-thought responses and chaining together models fine-tuned for subtasks have an enhanced ability to analyze and design DNA sequences and their structures.

Fluorescence is one of the most widely used techniques in biological sciences. Its exceptional sensitivity and versatility make it a tool of first choice for quantitative studies in biophysics. The concept of phasors, originally introduced by Charles Steinmetz in the late 19th century for analyzing alternating current circuits, has since found applications across diverse disciplines, including fluorescence spectroscopy. The main idea behind fluorescence phasors was posited by Gregorio Weber in 1981. By analyzing the complementary nature of pulse and phase fluorometry data, he shows that two magnitudes -- denoted as $G$ and $S$ -- derived from the frequency-domain fluorescence measurements correspond to the real and imaginary part of the Fourier transform of the fluorescence intensity in the time domain. This review provides a historical perspective on how the concept of phasors originates and how it integrates into fluorescence spectroscopy. We discuss their fundamental algebraic properties, which enable intuitive model-free analysis of fluorescence data despite the complexity of the underlying phenomena. Some applications in biophysics illustrate the power of this approach in stud

The network of biochemical reactions inside living organisms is characterized by an overwhelming complexity which stems from the sheer number of reactions and from the complicated topology of biochemical cycles. However the high speed of computers and the sophisticated computational methods that are available today are powerful tools that allow the numerical exploration of these exceedingly interesting dynamical systems. We are now developing a program, the Virtual Biophysics Lab (VBL), that simulates tumor spheroids, and which includes a reduced - but still quite complex - description of the biochemistry of individual cells, plus many diffusion processes that bring oxygen and nutrients into cells and metabolites into the environment. Each simulation step requires the integration of nonlinear differential equations that describe the individual cell's clockwork and the integration of the diffusion equations. These integrations are carried out under widely different conditions, in a changing environment, and for this reason they need integrators that are both unconditionally stable and that do not display unwanted algorithmic artifacts. These conditions are not always fulfilled in th

Here, we discuss a collection of cutting-edge techniques and applications in use today by some of the leading experts in the field of correlative approaches in single-molecule biophysics. A key difference in emphasis, compared with traditional single-molecule biophysics approaches detailed previously, is on the emphasis of the development and use of complex methods which explicitly combine multiple approaches to increase biological insights at the single-molecule level. These so-called correlative single-molecule biophysics methods rely on multiple, orthogonal tools and analysis, as opposed to any one single driving technique. Importantly, they span both in vivo and in vitro biological systems as well as the interfaces between theory and experiment in often highly integrated ways, very different to earlier traditional non-integrative approaches. The first applications of correlative single-molecule methods involved adaption of a range of different experimental technologies to the same biological sample whose measurements were synchronised. However, now we find a greater flora of integrated methods emerging that include approaches applied to different samples at different times and