Advances in protein design and the use of AI for predicting protein structures made the headlines with the 2024 Nobel Prize in Chemistry. But closer to home, researchers at Khalifa University in Abu Dhabi are leading the way in using computational methods to predict the crystal structures and properties of materials.

This foundational work is driving progress in energy storage, drug development and the creation of components for advanced optoelectronic devices.

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“The basic idea is to use computers to predict the atomic arrangement of solids before we synthesize them in the lab,” says Sharmarke Mohamed, head of the Chemical Crystallography Laboratory (CCL) at Khalifa University. “If we can do this accurately for all target molecules of interest, then this gets us one step closer to answering the scientifically interesting question of what experimental conditions are necessary to target the crystallization of a material with this particular structure.”

Using computers is time-saving, cost-effective and minimizes trial-and-error experiments. But why is this important?

Crystallizing proteins allows scientists to understand their structure in detail.

Proteins are complex macromolecules, and their shape determines how they function in the body. By creating crystals of proteins, researchers can use techniques like X-ray crystallography to study their 3D structure. This helps in designing medicines that fit a protein perfectly to treat diseases. It also advances understanding of conditions like cancer and Alzheimer’s by revealing malfunctions in the protein structure.

CAPTION: Sharmarke Mohamed (from left), Praveen Managutti and Thomas Delclos

“Fifteen years ago, when I was doing my Ph.D. in chemical crystallography and computational structure prediction, the question of whether computers can predict crystal structures was still an open question. The problem was also somewhat niche and confined to the academic community because very few industrial researchers were engaged in method development and testing. Today, most pharmaceutical companies around the world have some sort of computational crystal structure prediction research program in-house,” Mohamed says.

But the field has developed immensely over the past couple of decades thanks to a little healthy competition.

Critical Assessment of Structure Prediction (CASP) is a biennial event where researchers assess the performance of methods used to predict protein structures. Scientists worldwide participate in testing algorithms that aim to determine how proteins fold into their 3D shapes based solely on their amino acid sequences. Given the importance of protein structure in areas like drug development and disease research, CASP plays a critical role in advancing computer-based biology research and guiding improvements in prediction methods.

A similar blind test has been ongoing since 1999 for assessing progress in using computers to predict the crystal structures of small molecules.

The Crystal Structure Prediction (CSP) Blind Tests, organized by the Cambridge Crystallographic Data Centre, bring together scientists from academia and industry to evaluate their methods on real-world examples in a controlled setting. These tests also foster collaboration within the CSP community.

Mohamed and his team — including M.Sc. student Mubarak Almehairbi, Ph.D. student Zeinab Saeed and postdoctoral research fellows Tamador Alkhadir and Bhausaheb Dhokale — participated in the most recent CSP blind test.

“This seventh blind test featured the most challenging target molecules to date,” Mohamed tells KUST Review. “The results show that the field has progressed significantly since the first blind test in 1999, as reflected in the success rate in both structure generation and ranking. But as with all advancements in science, when we make progress in one area, new questions and challenges arise.

“Today, the challenge is not whether we can use computers to predict crystal structures, but how the predicted crystal structures can be used to guide experiments in the synthesis and discovery of functional materials,” Mohamed says. “This is now the focus of many researchers in the field, including our group in the Chemistry Department of Khalifa University.”

For example, machine learning has improved how we rank predicted crystal structures, helping researchers identify which ones are likely to form successfully under normal temperature and pressure conditions.

Ranking crystal structures helps researchers figure out which ones are most likely to be observed under real-life conditions. This saves time and effort by focusing on the best options for experiments.

Mohamed’s group is developing new methods and codes to help experiments target new materials with desirable solid-state properties. For example, the team recently created the MechaPredict code, which is able to predict the mechanical properties of crystals on any surface of interest without the need for sensitive nanoindentation experiments.

CAPTION: MechaPredict code summary IMAGE: Khalifa University

This code is already being used by academics around the world and has attracted interest from pharmaceutical companies for its potential to extend the shelf life and improve the solubility and stability of drug products. Additionally, the code can be applied in designing new materials like hole-transport layers for solar cells, which can lead to more efficient, versatile, cost-effective and longer-lasting solar panels.

But with all the advances made in computational CSP methods, a well-equipped crystallography laboratory is necessary to validate the accuracy of the computational predictions.

“The Chemical Crystallography Laboratory (CCL) is the best-equipped crystallography lab in the UAE for performing single-crystal X-ray diffraction, the gold standard for determining the crystal structures of materials,” Mohamed says. “The CCL provides experimental crystallographic services to Khalifa University researchers as well as to collaborators in the UAE and around the world. The synergy between experimental chemical crystallography and computational CSP methods is the key to seeing further advances such as those recognized in the 2024 Nobel Prize in Chemistry.”