Hasil untuk "q-bio.BM"

R. O’neil

J. Block

S. Nukiyama

Hocheol Lim, Geon-Ho Lee, Kyoung Tai No

Proteins play essential roles in nature, from catalyzing biochemical reactions to binding specific targets. Advances in protein engineering have the potential to revolutionize biotechnology and healthcare by designing proteins with tailored properties. Machine learning and generative models have transformed protein design by enabling the exploration of vast sequence-function landscapes. Here, we introduce Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot), a framework that iteratively combines autoregressive protein generation with quantitative structure-property relationship models for fine-tuned optimization. By integrating diverse protein descriptors, SAGE-Prot enhances key properties, including binding affinity, thermal stability, enzymatic activity, and solubility. We demonstrate its effectiveness by optimizing GB1 for binding affinity and thermal stability and TEM-1 for enzymatic activity and solubility. Leveraging curriculum learning, SAGE-Prot adapts rapidly to increasingly complex design objectives, building on past successes. Experimental validation demonstrated that SAGE-Prot-generated proteins substantially outperformed their wild-type counterparts, achieving up to a 17-fold increase in beta-lactamase activity, underscoring SAGE-Prot's potential to tackle critical challenges in protein engineering. As generative models continue to evolve, approaches like SAGE-Prot will be indispensable for advancing rational protein design.

Xinzhe Zheng, Shiyu Jiang, Gustavo Seabra et al.

Deep generative models are rapidly advancing structure-based drug design, offering substantial promise for generating small molecule ligands that bind to specific protein targets. However, most current approaches assume a rigid protein binding pocket, neglecting the intrinsic flexibility of proteins and the conformational rearrangements induced by ligand binding, limiting their applicability in practical drug discovery. Here, we propose Apo2Mol, a diffusion-based generative framework for 3D molecule design that explicitly accounts for conformational flexibility in protein binding pockets. To support this, we curate a dataset of over 24,000 experimentally resolved apo-holo structure pairs from the Protein Data Bank, enabling the characterization of protein structure changes associated with ligand binding. Apo2Mol employs a full-atom hierarchical graph-based diffusion model that simultaneously generates 3D ligand molecules and their corresponding holo pocket conformations from input apo states. Empirical studies demonstrate that Apo2Mol can achieve state-of-the-art performance in generating high-affinity ligands and accurately capture realistic protein pocket conformational changes.

B. Mazur, A. Wiles

Zhiwei Wang, Yongkang Wang, Wen Zhang

Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information present in multi-modal data, and most methods predict paratopes and epitopes separately, overlooking their specific spatial interactions. In this paper, we propose a novel Multi-modal contrastive learning and Interaction informativeness estimation-based method for Paratope and Epitope prediction, named MIPE, by using both sequence and structure data of antibodies and antigens. MIPE implements a multi-modal contrastive learning strategy, which maximizes representations of binding and non-binding residues within each modality and meanwhile aligns uni-modal representations towards effective modal representations. To exploit the spatial interaction information, MIPE also incorporates an interaction informativeness estimation that computes the estimated interaction matrices between antibodies and antigens, thereby approximating them to the actual ones. Extensive experiments demonstrate the superiority of our method compared to baselines. Additionally, the ablation studies and visualizations demonstrate the superiority of MIPE owing to the better representations acquired through multi-modal contrastive learning and the interaction patterns comprehended by the interaction informativeness estimation.

Jiaxing Yang

Structural prediction has long been considered critical in RNA research, especially following the success of AlphaFold2 in protein studies, which has drawn significant attention to the field. While recent advances in machine learning and data accumulation have effectively addressed many biological tasks, particularly in protein related research. RNA structure prediction remains a significant challenge due to data limitations. Obtaining RNA structural data is difficult because traditional methods such as nuclear magnetic resonance spectroscopy, Xray crystallography, and electron microscopy are expensive and time consuming. Although several RNA 3D structure prediction methods have been proposed, their accuracy is still limited. Predicting RNA structural information at another level, such as distance maps, remains highly valuable. Distance maps provide a simplified representation of spatial constraints between nucleotides, capturing essential relationships without requiring a full 3D model. This intermediate level of structural information can guide more accurate 3D modeling and is computationally less intensive, making it a useful tool for improving structural predictions. In this work, we demonstrate that using only primary sequence information, we can accurately infer the distances between RNA bases by utilizing a large pretrained RNA language model coupled with a well trained downstream transformer.

Carla Alejandre, Adrián Aguirre-Tamaral, Carlos Briones et al.

In the study of life's origins, a key challenge is understanding how RNA could have polymerized and subsequently replicated in early Earth. We present a theoretical and computational framework to model the non-enzymatic polymerization of ribonucleotides and the template-dependent replication of primordial RNA molecules, at the interfaces between the aqueous solution and a clay mineral. Our results demonstrate that systematic polymerization and replication of single-stranded RNA polymers, sufficiently long to fold and acquire basic functions ($>$15 nt), were feasible under these conditions. Crucially, this process required a physico-chemical environment characterized by large-amplitude oscillations with periodicity compatible with spring tide dynamics, suggesting that large moons may have played a role in the emergence of RNA-based life on planetary bodies. Interestingly, the theoretical analysis presents rigorous evidence that RNA replication efficiency increases in oscillating environments compared to constant ones. Moreover, the versatility of our framework enables comparisons between different genetic alphabets, showing that a four-letter alphabet -- particularly when allowing non-canonical base pairs, as in current RNA -- represents an optimal balance of replication speed and sequence diversity in the pathway to life.

K. Ribet

O. Aharony, A. Hanany, B. Kol

We continue to study 5d N = 1 supersymmetric field theories and their compactifications on a circle through brane configurations. We develop a model, which we call (p,q) Webs, which enables simple geometrical computations to reproduce the known results, and facilitates further study. The physical concepts of field theory are transparent in this picture, offering an interpretation for global symmetries, local symmetries, the effective (running) coupling, the Coulomb and Higgs branches, the monopole tensions, and the mass of BPS particles. A rule for the dimension of the Coulomb branch is found by introducing Grid Diagrams. Some known classifications of field theories are reproduced. In addition to the study of the vacuum manifold we develop methods to determine the BPS spectrum. Some states, such as quarks, correspond to instantons inside the 5-brane which we call strips. In general, these may not be identified with (p,q) strings. We describe how a strip can bend out of a 5-brane, becoming a string. A general BPS state corresponds to a Web of strings and strips. For special values of the string coupling a few strips can combine and leave the 5-brane as a string.

A. Zambon, R. Zecchina, G. Tiana

Proteins populate a manifold in the high-dimensional sequence space whose geometrical structure guides their natural evolution. Leveraging recently-developed structure prediction tools based on transformer models, we first examine the protein sequence landscape as defined by the folding score function. This landscape shares characteristics with optimization challenges encountered in machine learning and constraint satisfaction problems. Our analysis reveals that natural proteins predominantly reside in wide, flat minima within this energy landscape. To investigate further, we employ statistical mechanics algorithms specifically designed to explore regions with high local entropy in relatively flat landscapes. Our findings indicate that these specialized algorithms can identify valleys with higher entropy compared to those found using traditional methods such as Monte Carlo Markov Chains. In a proof-of-concept case, we find that these highly entropic minima exhibit significant similarities to natural sequences, especially in critical key sites and local entropy. Additionally, evaluations through Molecular Dynamics suggests that the stability of these sequences closely resembles that of natural proteins. Our tool combines advancements in machine learning and statistical physics, providing new insights into the exploration of sequence landscapes where wide, flat minima coexist alongside a majority of narrower minima.

F. Cardano, E. Karimi, S. Slussarenko et al.

We describe the polarization topology of the vector beams emerging from a patterned birefringent liquid crystal plate with a topological charge q at its center (q-plate). The polarization topological structures for different q-plates and different input polarization states have been studied experimentally by measuring the Stokes parameters point-by-point in the beam transverse plane. Furthermore, we used a tuned q=1/2-plate to generate cylindrical vector beams with radial or azimuthal polarizations, with the possibility of switching dynamically between these two cases by simply changing the linear polarization of the input beam.

G. Mitchell, A. Jeron, G. Koren

Magnus Bentinger, K. Brismar, G. Dallner

M. Kashiwara, Toshiki Nakashima

S. Jowett, N. Ntoumanis

R. Cross

D. Warburton, V. Jamnik, S. Bredin et al.

Nathalie Arricau-Bouvery, A. Rodolakis