Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe + 24 more
There has been considerable recent progress in designing new proteins using deep-learning methods1-9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric…
Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Neil Tenenholtz + 6 more
Deep generative models are increasingly powerful tools for the in silico design of novel proteins. Recently, a family of generative models called diffusion models has demonstrated the ability to generate biologically plausible proteins that are dissimilar to any actual proteins seen in nature, enabling unprecedented…
Susana Vázquez Torres, Philip J. Y. Leung, Preetham Venkatesh, Isaac D. Lutz + 26 more
Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions…
Yujie Qin, Ming He, Changyong Yu, Ming Ni + 2 more
The de novo design of proteins refers to creating proteins with specific structures and functions that do not naturally exist. In recent years, the accumulation of high-quality protein structure and sequence data and technological advancements have paved the way for the successful application of generative artificial…
Matthias Glögl, Aditya Krishnakumar, Robert J. Ragotte, Inna Goreshnik + 11 more
Despite progress in designing protein binding proteins, the shape matching of designs to targets is lower than in many native protein complexes, and design efforts have failed for TNF receptor (TNFR1) and other protein targets with relatively flat and polar surfaces. We hypothesized that free diffusion from random…
Amelia Villegas-Morcillo, Gijs J. Admiraal, Marcel J. T. Reinders, Jana M. Weber
Advancing protein design is crucial for breakthroughs in medicine and biotechnology. Traditional approaches for protein sequence representation often rely solely on the 20 canonical amino acids, limiting the representation of non-canonical amino acids and residues that undergo post-translational modifications. This…
Amelia Villegas-Morcillo, Gijs J. Admiraal, Marcel J.T. Reinders, Jana M. Weber
Advancing protein design is crucial for breakthroughs in medicine and biotechnology. Traditional approaches for protein sequence representation often rely solely on the 20 canonical amino acids, limiting the representation of non-canonical amino acids and residues that undergo post-translational modifications. This…
W. B. Li, Xavier F. Cadet, David Medina-Ortiz, Mehdi D. Davari + 5 more
generation towards autonomous protein engineering Authors: ['W. B. Li' 'Xavier F. Cadet' 'David Medina-Ortiz' 'Mehdi D. Davari' 'Ramanathan Sowdhamini' 'Cédric Damour' 'Li Yu' 'Alain Miranville' 'Frédéric Cadet'] Protein design with desirable properties has been a significant challenge for many decades. Generative…
Yufeng Liu, Linghui Chen, Quan Chen, Haiyan Liu
Conformational dynamics are often critical for protein functions. There is strong interest in deep learning models to predict the conformational distributions of proteins or design protein structures that can host rich conformational dynamics. Here we report PVQD (Protein Vector Quantization and Diffusion), a method…
Chentong Wang, Yannan Qu, Zhangzhi Peng, Yukai Wang + 3 more
The development of de novo protein design method is crucial for widespread applications in biology and chemistry. Protein backbone diffusion aims to generate designable protein structures with high efficiency. Although there have been great strides in protein structure prediction, applying these methods to protein…
Dylan Silke, Julie Iskander, Junqi Pan, Andrew P. Thompson + 3 more
Leveraging artificial intelligence and deep learning to generate proteins de novo (a.k.a. ‘synthetic proteins’) has unlocked new frontiers of protein design. Deep learning models trained on protein structures can generate novel protein designs that explore structural landscapes unseen by evolution. This approach…
Adam Winnifrith, Carlos Outeiral, Brian Hie
Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the socalled "de novo" design problem have recently been brought forward by developments in artificial intelligence. Generative architectures…
Shaowen Zhu, Siddhant Gulati, Yuxuan Liu, Siddhi Kotnis + 2 more
Computational design of functional proteins is of both fundamental and applied interest. This study introduces a generative framework for co-designing protein sequence and structure in a unified process by modeling their joint distribution, with the goal of enabling cross-modality interactions toward coherent and…
Bo Ni, Markus J. Buehler
Language Diffusion Model Authors: ['Bo Ni' 'Markus J. Buehler'] Abstract: Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their motions. Designing proteins with targeted dynamic properties…
Authors not listed
Generation of de novo peptides docking snake three-fold α-neurotoxins (3Ftx) were designed using computational diffusion models as alternatives to screening or designing larger antibody/protein-based and smaller drug-like anti-venoms. To approach the high variability of snake venoms, the previously described…
Authors not listed
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a…
Ilya A. Vakser, Sergei Grudinin, Nathan W. Jenkins, Petras J. Kundrotas + 1 more
'Petras J. Kundrotas' 'Eric J. Deeds'] Title: Significance Advances in computational modeling have led to an increasing focus on larger biomolecular systems, up to the level of a cell. Protein interactions are a central component of cellular processes. Techniques for modeling protein interactions have been divided…
David Mignon, Karen Druart, Vaitea Opuu, Savvas Polydorides + 5 more
We describe methods and software for physics-based protein design. The folded state energy combines molecular mechanics with Generalized Born solvent. Sequence and conformation space are sampled with Replica Exchange Monte Carlo, assuming one or a few fixed protein backbone structures and discrete side chain rotamers.…
Paolo Mereghetti, Daria Kokh, J Andrew McCammon, Rebecca C Wade
Macromolecular diffusion plays a fundamental role in biological processes. Here, we give an overview of recent methodological advances and some of the challenges for understanding how molecular diffusional properties influence biological function that were highlighted at a recent workshop, BDBDB2, the second Biological…
Preet Kalani, Vojtěch Spiwok
Design of new proteins is often formulated as an optimization task. An amino acid sequence is characterized by an energy, and this energy is sampled and minimized. Here, we use a parallel tempering algorithm to accelerate this task. A series of 100- or 200-residue proteins was designed using a modified Evolutionary…
Giancarlo Franzese, Joan Àguila Rojas, Valentino Bianco, Ivan Coluzza
'Ivan Coluzza'] Abstract Water plays a fundamental role in protein stability. However, the effect of the properties of water on the behaviour of proteins is only partially understood. Several theories have been proposed to give insight into the mechanisms of cold and pressure denaturation, or the limits of temperature…
Authors not listed
Free energy calculations have become invaluable in protein design, offering a powerful means to rapidly and accurately screen potential variants. Here, we provide a step-by-step protocol that combines molecular dynamics simulations with non-equilibrium alchemical free energy methods to tackle open questions in protein…
Authors not listed
We review the use of state-of-the-art enhanced sampling techniques in molecular dynamics (MD) simu- lations. We briefly introduce the principles and practical considerations underlying the application of these methods, making reference to recent reviews, and then discuss their application to membrane transporters as…
Authors not listed
The formation and modulation of biomolecular condensates as well as their structural and dynamic properties are determined by an intricate interplay of different driving forces, which down at the microscopic scale involve molecular interactions of the biological macromolecules and the surrounding solvent and ions.…
Marta Chronowska, Michael J. Stam, Derek N. Woolfson, Luigi F. Di Constanzo + 1 more
The field of protein design has changed dramatically over the last 40 years, with a range of methods developing from rational design to more recent data-driven approaches. While considerable insight could be gained from analysing designed proteins, there is no single resource that brings together all the relevant data…
Ahmed A. A. I. Ali, Falk Hoffmann, Lars V. Schäfer, Frans A. A. Mulder
Nuclear magnetic resonance (NMR) spin relaxation is the most informative approach to experimentally probe the internal dynamics of proteins on the picosecond to nanosecond timescale. At the same time, molecular dynamics (MD) simulations of biological macromolecules are steadily improving through better physical models…
Lunna Li, Tommaso Casalini, Paolo Arosio, Matteo Salvalaglio
Intrinsically disordered proteins (IDPs) play a key role in many biological processes, including the formation of biomolecular condensates within cells. A detailed characterization of their configurational ensemble and structure-function paradigm is crucial for understanding their biological activity and for exploiting…