Cryo-electron microscopy can now routinely deliver atomic resolution structures for a variety of biological systems. The relevance and value of these structures are directly related to their ability…

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Join us on Thursday October 2nd to connect with the computational chemistry community in the San Francisco Bay Area and hear about cool science! We will have time for networking, followed by 2 excit...

AlphaFold (AF) models are widely used in cryo-EM workflows, but their static structure predictions limit the interpretation of flexible or unresolved regions. To address this, we investigate whether A...

Motivation: AlphaFold2 significantly improved the prediction of protein complex structures. However, its accuracy is lower for interactions without co-evolutionary signals, such as host-pathogen and a...

Postdoctoral Scientist Salary £41,344 per annum  Fixed Term – 3 years MRC Laboratory of Molecular Biology, Cambridge, UK We are looking for a postd...

Abstract . Comparing and searching protein structures independent of primary sequence has proved useful for remote homology detection, function annotation,

In drug discovery, the relevance and value of protein structures is directly related to their ability to rationally optimize molecular properties.

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Proteins exist as a dynamic ensemble of multiple conformations, and these motions are often crucial for their functions. However, current structure prediction methods predominantly yield a single conf...

Join our team and contribute to the development of HADDOCK, our integrative modelling software, as part of an exciting EU-India research collaboration.

The SBGrid Consortium is an innovative global research computing group operated out of Harvard Medical School. SBGrid provides the global structural biology community with support for research…

Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with other molecules, especially small mo...

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image image Common sources of bias in variant effect predictor benchmarking are assessed using data from deep mutational scanning experiments. ESM‐1v, EVE and DeepSequence are among the top performers...

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecu...

In machine learning approaches relevant for chemical physics and material science, neural network potentials can be trained on the experimental data. The authors propose a training method applying tra...

We use an unrolled simulator as an end-to-end differentiable model of protein structure and show it can (sometimes) hierarchically generalize to unseen fold topologies.

Abstract A half century of studying protein folding in vitro and modeling it in silico has not provided us with a reliable computational method to predict the native conformations of proteins de novo,...

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

Combining metagenome data with protein structure prediction generates models for 614 families with unknown structures.

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Cycles of design, building, and testing produced a 531-kilobase genome comprising 473 genes.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algo...

The development of accurate molecular mechanics force fields is a significant challenge that must be addressed for the continued success of molecular simulation. We developed the ForceBalance method to automatically derive accurate force field parameters using flexible combinations of experimental and theoretical reference data. The method is demonstrated in the parametrization of two rigid water models, yielding new parameter sets (TIP3P-FB and TIP4P-FB) that accurately describe many physical properties of water.

We present a simple Python library to construct models of polypeptides from scratch. The intended use case is the generation of peptide models with pre-specified backbone angles. For example, using ou...